{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/graph-learning/papers/ran/2","list_of":"/task/graph-learning","task":"Graph Learning","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":2,"pages_in_order":2,"rows_per_page":100,"rows":[101,193],"of":193,"counts":{"archive_papers_tagged":1570,"with_a_code_link":686,"where_syntology_ran_a_sample":193,"not_listed_spam_title":0,"listed":1570,"listed_where_code_ran":193,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":162,"every_run_a_failure_of_syntologys_instrument":31,"listed_with_a_run_with_no_instrument_failure":162,"listed_every_run_a_failure_of_syntologys_instrument":31,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/graph-learning/papers/ran/1","prev":"/task/graph-learning/papers/ran/1","next":null,"papers":[{"url":"/paper/multi-modal-graph-learning-over-umls","slug":"multi-modal-graph-learning-over-umls","title":"Multi-modal Graph Learning over UMLS Knowledge Graphs","date":"2023-07-10","arxiv_id":"2307.04461","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multi-modal-graph-learning-over-umls#ran","syntology_url":"https://syntology.ai/paper/2307.04461","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.04461"}},"official":{"repos":["ratschlab/mmugl"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-heterogeneous-graph-learning-with","slug":"improving-heterogeneous-graph-learning-with","title":"Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product Manifold","date":"2023-07-10","arxiv_id":"2307.04514","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/improving-heterogeneous-graph-learning-with#ran","syntology_url":"https://syntology.ai/paper/2307.04514","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.04514"}},"official":{"repos":["sharecodesubmission/weighted_product_manifold"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/knowledge-graph-self-supervised","slug":"knowledge-graph-self-supervised","title":"Knowledge Graph Self-Supervised Rationalization for Recommendation","date":"2023-07-06","arxiv_id":"2307.02759","repositories_listed":1,"syntology":{"n":8,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/knowledge-graph-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2307.02759","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.02759"}},"official":{"repos":["hkuds/kgrec"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/re-think-and-re-design-graph-neural-networks","slug":"re-think-and-re-design-graph-neural-networks","title":"Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals","date":"2023-07-01","arxiv_id":"2307.00222","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/re-think-and-re-design-graph-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2307.00222","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.00222"}},"official":{"repos":["Dandy5721/GNN-PDE-COV"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/individual-and-structural-graph-information","slug":"individual-and-structural-graph-information","title":"Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization","date":"2023-06-28","arxiv_id":"2306.15902","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/individual-and-structural-graph-information#ran","syntology_url":"https://syntology.ai/paper/2306.15902","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.15902"}},"official":{"repos":["yangling0818/graphood"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/spatial-temporal-graph-learning-with","slug":"spatial-temporal-graph-learning-with","title":"Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation","date":"2023-06-19","arxiv_id":"2306.10683","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":2,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/spatial-temporal-graph-learning-with#ran","syntology_url":"https://syntology.ai/paper/2306.10683","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.10683"}},"official":{"repos":["hkuds/graphst"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/automated-3d-pre-training-for-molecular","slug":"automated-3d-pre-training-for-molecular","title":"Automated 3D Pre-Training for Molecular Property Prediction","date":"2023-06-13","arxiv_id":"2306.07812","repositories_listed":1,"syntology":{"n":13,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":3,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/automated-3d-pre-training-for-molecular#ran","syntology_url":"https://syntology.ai/paper/2306.07812","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.07812"}},"official":{"repos":["lars-research/3d-pgt"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/expectation-complete-graph-representations","slug":"expectation-complete-graph-representations","title":"Expectation-Complete Graph Representations with Homomorphisms","date":"2023-06-09","arxiv_id":"2306.05838","repositories_listed":2,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/expectation-complete-graph-representations#ran","syntology_url":"https://syntology.ai/paper/2306.05838","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.05838"}},"official":{"repos":["ocatias/homcountgnns","pwelke/homcount"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/structure-free-graph-condensation-from-large","slug":"structure-free-graph-condensation-from-large","title":"Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data","date":"2023-06-05","arxiv_id":"2306.02664","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/structure-free-graph-condensation-from-large#ran","syntology_url":"https://syntology.ai/paper/2306.02664","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.02664"}},"official":{"repos":["amanda-zheng/sfgc"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/graphcleaner-detecting-mislabelled-samples-in","slug":"graphcleaner-detecting-mislabelled-samples-in","title":"GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks","date":"2023-05-30","arxiv_id":"2306.00015","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/graphcleaner-detecting-mislabelled-samples-in#ran","syntology_url":"https://syntology.ai/paper/2306.00015","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.00015"}},"official":{"repos":["lywww/graphcleaner"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-strong-graph-neural-networks-with","slug":"learning-strong-graph-neural-networks-with","title":"Learning Strong Graph Neural Networks with Weak Information","date":"2023-05-29","arxiv_id":"2305.18457","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-strong-graph-neural-networks-with#ran","syntology_url":"https://syntology.ai/paper/2305.18457","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18457"}},"official":{"repos":["yixinliu233/d2pt"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/confidence-based-feature-imputation-for","slug":"confidence-based-feature-imputation-for","title":"Confidence-Based Feature Imputation for Graphs with Partially Known Features","date":"2023-05-26","arxiv_id":"2305.16618","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":5,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/confidence-based-feature-imputation-for#ran","syntology_url":"https://syntology.ai/paper/2305.16618","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.16618"}},"official":{"repos":["daehoum1/pcfi"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-neural-convection-diffusion-with","slug":"graph-neural-convection-diffusion-with","title":"Graph Neural Convection-Diffusion with Heterophily","date":"2023-05-26","arxiv_id":"2305.16780","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/graph-neural-convection-diffusion-with#ran","syntology_url":"https://syntology.ai/paper/2305.16780","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.16780"}},"official":{"repos":["zknus/graph-diffusion-cde"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/joint-feature-and-differentiable-k-nn-graph","slug":"joint-feature-and-differentiable-k-nn-graph","title":"Joint Feature and Differentiable $ k $-NN Graph Learning using Dirichlet Energy","date":"2023-05-21","arxiv_id":"2305.12396","repositories_listed":0,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/joint-feature-and-differentiable-k-nn-graph#ran","syntology_url":"https://syntology.ai/paper/2305.12396","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.12396"}},"official":null}},{"url":"/paper/graph-propagation-transformer-for-graph","slug":"graph-propagation-transformer-for-graph","title":"Graph Propagation Transformer for Graph Representation Learning","date":"2023-05-19","arxiv_id":"2305.11424","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":4,"n_ran_checked":4,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":8,"phrase":"4 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","sample_list":"/paper/graph-propagation-transformer-for-graph#ran","syntology_url":"https://syntology.ai/paper/2305.11424","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.11424"}},"official":{"repos":["czczup/gptrans"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/fisher-information-embedding-for-node-and","slug":"fisher-information-embedding-for-node-and","title":"Fisher Information Embedding for Node and Graph Learning","date":"2023-05-12","arxiv_id":"2305.07580","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fisher-information-embedding-for-node-and#ran","syntology_url":"https://syntology.ai/paper/2305.07580","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.07580"}},"official":{"repos":["BorgwardtLab/fisher_information_embedding"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-better-graph-representation-learning","slug":"towards-better-graph-representation-learning","title":"Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering","date":"2023-05-10","arxiv_id":"2305.06102","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/towards-better-graph-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2305.06102","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.06102"}},"official":{"repos":["qslim/pdf"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-better-dynamic-graph-learning-new-1","slug":"towards-better-dynamic-graph-learning-new-1","title":"Towards Better Dynamic Graph Learning: New Architecture and Unified Library","date":"2023-03-23","arxiv_id":"2303.13047","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/towards-better-dynamic-graph-learning-new-1#ran","syntology_url":"https://syntology.ai/paper/2303.13047","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.13047"}},"official":{"repos":["yule-buaa/dyglib"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/easydgl-encode-train-and-interpret-for","slug":"easydgl-encode-train-and-interpret-for","title":"EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning","date":"2023-03-22","arxiv_id":"2303.12341","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/easydgl-encode-train-and-interpret-for#ran","syntology_url":"https://syntology.ai/paper/2303.12341","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.12341"}},"official":{"repos":["cchao0116/EasyDGL"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/dynamically-expandable-graph-convolution-for","slug":"dynamically-expandable-graph-convolution-for","title":"Dynamically Expandable Graph Convolution for Streaming Recommendation","date":"2023-03-21","arxiv_id":"2303.11700","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dynamically-expandable-graph-convolution-for#ran","syntology_url":"https://syntology.ai/paper/2303.11700","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.11700"}},"official":{"repos":["bokwaiho/degc"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/exphormer-sparse-transformers-for-graphs","slug":"exphormer-sparse-transformers-for-graphs","title":"Exphormer: Sparse Transformers for Graphs","date":"2023-03-10","arxiv_id":"2303.06147","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/exphormer-sparse-transformers-for-graphs#ran","syntology_url":"https://syntology.ai/paper/2303.06147","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.06147"}},"official":{"repos":["hamed1375/exphormer"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/multiresolution-graph-transformers-and","slug":"multiresolution-graph-transformers-and","title":"Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical Structures","date":"2023-02-17","arxiv_id":"2302.08647","repositories_listed":2,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/multiresolution-graph-transformers-and#ran","syntology_url":"https://syntology.ai/paper/2302.08647","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.08647"}},"official":{"repos":["hysonlab/multires-graph-transformer","vijaydwivedi75/lrgb"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/outlier-robust-gromov-wasserstein-for-graph-1","slug":"outlier-robust-gromov-wasserstein-for-graph-1","title":"Outlier-Robust Gromov-Wasserstein for Graph Data","date":"2023-02-09","arxiv_id":"2302.04610","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/outlier-robust-gromov-wasserstein-for-graph-1#ran","syntology_url":"https://syntology.ai/paper/2302.04610","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.04610"}},"official":{"repos":["lmkong020/outlier-robust-gw"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/causal-bandits-without-graph-learning","slug":"causal-bandits-without-graph-learning","title":"Causal Bandits without Graph Learning","date":"2023-01-26","arxiv_id":"2301.11401","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/causal-bandits-without-graph-learning#ran","syntology_url":"https://syntology.ai/paper/2301.11401","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.11401"}},"official":{"repos":["borealisai/raps"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deepdfa-dataflow-analysis-guided-efficient","slug":"deepdfa-dataflow-analysis-guided-efficient","title":"Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection","date":"2022-12-15","arxiv_id":"2212.08108","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deepdfa-dataflow-analysis-guided-efficient#ran","syntology_url":"https://syntology.ai/paper/2212.08108","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.08108"}},"official":{"repos":["ISU-PAAL/DeepDFA"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-matching-with-bi-level-noisy","slug":"graph-matching-with-bi-level-noisy","title":"Graph Matching with Bi-level Noisy Correspondence","date":"2022-12-08","arxiv_id":"2212.04085","repositories_listed":3,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":1,"n_instrument":6,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 6 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/graph-matching-with-bi-level-noisy#ran","syntology_url":"https://syntology.ai/paper/2212.04085","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.04085"}},"official":{"repos":["Thinklab-SJTU/ThinkMatch","xlearning-scu/2023-iccv-common","Lin-Yijie/Graph-Matching-Networks"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-learning-on-non-iid-graphs-via","slug":"federated-learning-on-non-iid-graphs-via","title":"Federated Learning on Non-IID Graphs via Structural Knowledge Sharing","date":"2022-11-23","arxiv_id":"2211.13009","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":4,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 4 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/federated-learning-on-non-iid-graphs-via#ran","syntology_url":"https://syntology.ai/paper/2211.13009","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.13009"}},"official":{"repos":["yuetan031/fedstar"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-new-graph-node-classification-benchmark","slug":"a-new-graph-node-classification-benchmark","title":"A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphs","date":"2022-11-11","arxiv_id":"2211.06292","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-new-graph-node-classification-benchmark#ran","syntology_url":"https://syntology.ai/paper/2211.06292","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.06292"}},"official":{"repos":["nellaker-group/placenta"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/pygsl-a-graph-structure-learning-toolkit","slug":"pygsl-a-graph-structure-learning-toolkit","title":"pyGSL: A Graph Structure Learning Toolkit","date":"2022-11-07","arxiv_id":"2211.03583","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pygsl-a-graph-structure-learning-toolkit#ran","syntology_url":"https://syntology.ai/paper/2211.03583","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.03583"}},"official":{"repos":["maxwass/pygsl"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-few-shot-learning-with-task-specific","slug":"graph-few-shot-learning-with-task-specific","title":"Graph Few-shot Learning with Task-specific Structures","date":"2022-10-21","arxiv_id":"2210.12130","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/graph-few-shot-learning-with-task-specific#ran","syntology_url":"https://syntology.ai/paper/2210.12130","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12130"}},"official":{"repos":["songw-sw/glitter"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-practical-progressively-expressive-gnn","slug":"a-practical-progressively-expressive-gnn","title":"A Practical, Progressively-Expressive GNN","date":"2022-10-18","arxiv_id":"2210.09521","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-practical-progressively-expressive-gnn#ran","syntology_url":"https://syntology.ai/paper/2210.09521","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.09521"}},"official":{"repos":["lingxiaoshawn/kcsetgnn"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/geodesic-graph-neural-network-for-efficient","slug":"geodesic-graph-neural-network-for-efficient","title":"Geodesic Graph Neural Network for Efficient Graph Representation Learning","date":"2022-10-06","arxiv_id":"2210.02636","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/geodesic-graph-neural-network-for-efficient#ran","syntology_url":"https://syntology.ai/paper/2210.02636","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.02636"}},"official":{"repos":["woodcutter1998/gdgnn"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/position-aware-structure-learning-for-graph","slug":"position-aware-structure-learning-for-graph","title":"Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing","date":"2022-08-17","arxiv_id":"2208.08302","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/position-aware-structure-learning-for-graph#ran","syntology_url":"https://syntology.ai/paper/2208.08302","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.08302"}},"official":{"repos":["ringbdstack/pastel"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/semantic-enhanced-text-to-sql-parsing-via","slug":"semantic-enhanced-text-to-sql-parsing-via","title":"Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graph","date":"2022-08-08","arxiv_id":"2208.03903","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/semantic-enhanced-text-to-sql-parsing-via#ran","syntology_url":"https://syntology.ai/paper/2208.03903","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.03903"}},"official":{"repos":["thu-bpm/isesl-sql"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/pure-transformers-are-powerful-graph-learners","slug":"pure-transformers-are-powerful-graph-learners","title":"Pure Transformers are Powerful Graph Learners","date":"2022-07-06","arxiv_id":"2207.02505","repositories_listed":2,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":5,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 2 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pure-transformers-are-powerful-graph-learners#ran","syntology_url":"https://syntology.ai/paper/2207.02505","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.02505"}},"official":{"repos":["jw9730/tokengt"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-trees-with-attention","slug":"graph-trees-with-attention","title":"TREE-G: Decision Trees Contesting Graph Neural Networks","date":"2022-07-06","arxiv_id":"2207.02760","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/graph-trees-with-attention#ran","syntology_url":"https://syntology.ai/paper/2207.02760","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.02760"}},"official":{"repos":["mayabechlerspeicher/tree-g"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/autogml-fast-automatic-model-selection-for","slug":"autogml-fast-automatic-model-selection-for","title":"MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learning","date":"2022-06-18","arxiv_id":"2206.09280","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/autogml-fast-automatic-model-selection-for#ran","syntology_url":"https://syntology.ai/paper/2206.09280","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.09280"}},"official":{"repos":["namyongpark/metagl"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/all-the-world-s-a-hyper-graph-a-data-drama","slug":"all-the-world-s-a-hyper-graph-a-data-drama","title":"All the World's a (Hyper)Graph: A Data Drama","date":"2022-06-16","arxiv_id":"2206.08225","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/all-the-world-s-a-hyper-graph-a-data-drama#ran","syntology_url":"https://syntology.ai/paper/2206.08225","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.08225"}},"official":{"repos":["hyperbard/hyperbard","hyperbard/tutorials"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-surprising-behaviour-of-node2vec","slug":"on-the-surprising-behaviour-of-node2vec","title":"On the Surprising Behaviour of node2vec","date":"2022-06-16","arxiv_id":"2206.08252","repositories_listed":1,"syntology":{"n":10,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/on-the-surprising-behaviour-of-node2vec#ran","syntology_url":"https://syntology.ai/paper/2206.08252","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.08252"}},"official":{"repos":["aidos-lab/node2vec-surprises"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/nagphormer-neighborhood-aggregation-graph","slug":"nagphormer-neighborhood-aggregation-graph","title":"NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs","date":"2022-06-10","arxiv_id":"2206.04910","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/nagphormer-neighborhood-aggregation-graph#ran","syntology_url":"https://syntology.ai/paper/2206.04910","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.04910"}},"official":{"repos":["jhl-hust/nagphormer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/spatio-temporal-graph-few-shot-learning-with","slug":"spatio-temporal-graph-few-shot-learning-with","title":"Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer","date":"2022-05-27","arxiv_id":"2205.13947","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/spatio-temporal-graph-few-shot-learning-with#ran","syntology_url":"https://syntology.ai/paper/2205.13947","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.13947"}},"official":{"repos":["robinlu1209/st-gfsl"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/sparse-graph-learning-for-spatiotemporal-time","slug":"sparse-graph-learning-for-spatiotemporal-time","title":"Sparse Graph Learning from Spatiotemporal Time Series","date":"2022-05-26","arxiv_id":"2205.13492","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sparse-graph-learning-for-spatiotemporal-time#ran","syntology_url":"https://syntology.ai/paper/2205.13492","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.13492"}},"official":{"repos":["andreacini/sparse-graph-learning"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/graphmae-self-supervised-masked-graph","slug":"graphmae-self-supervised-masked-graph","title":"GraphMAE: Self-Supervised Masked Graph Autoencoders","date":"2022-05-22","arxiv_id":"2205.10803","repositories_listed":3,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/graphmae-self-supervised-masked-graph#ran","syntology_url":"https://syntology.ai/paper/2205.10803","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.10803"}},"official":{"repos":["thudm/graphmae"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/kgtuner-efficient-hyper-parameter-search-for","slug":"kgtuner-efficient-hyper-parameter-search-for","title":"KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning","date":"2022-05-05","arxiv_id":"2205.02460","repositories_listed":2,"syntology":{"n":19,"n_ran":11,"n_constructed":3,"n_ran_checked":6,"n_instrument":5,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":18,"phrase":"11 ran (of which 3 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 5 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/kgtuner-efficient-hyper-parameter-search-for#ran","syntology_url":"https://syntology.ai/paper/2205.02460","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.02460"}},"official":{"repos":["automl-research/kgtuner"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/hypergraph-convolutional-networks-via-1","slug":"hypergraph-convolutional-networks-via-1","title":"Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs","date":"2022-03-31","arxiv_id":"2203.16939","repositories_listed":2,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/hypergraph-convolutional-networks-via-1#ran","syntology_url":"https://syntology.ai/paper/2203.16939","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.16939"}},"official":{"repos":["youjibiying/H-GNNs"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-modal-graph-learning-for-disease-1","slug":"multi-modal-graph-learning-for-disease-1","title":"Multi-modal Graph Learning for Disease Prediction","date":"2022-03-11","arxiv_id":"2203.05880","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multi-modal-graph-learning-for-disease-1#ran","syntology_url":"https://syntology.ai/paper/2203.05880","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.05880"}},"official":{"repos":["ssgood/mmgl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hyperbolic-graph-neural-networks-a-review-of","slug":"hyperbolic-graph-neural-networks-a-review-of","title":"Hyperbolic Graph Neural Networks: A Review of Methods and Applications","date":"2022-02-28","arxiv_id":"2202.13852","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hyperbolic-graph-neural-networks-a-review-of#ran","syntology_url":"https://syntology.ai/paper/2202.13852","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.13852"}},"official":{"repos":["marlin-codes/HIE"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hyla-hyperbolic-laplacian-features-for-graph","slug":"hyla-hyperbolic-laplacian-features-for-graph","title":"Random Laplacian Features for Learning with Hyperbolic Space","date":"2022-02-14","arxiv_id":"2202.06854","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":1,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hyla-hyperbolic-laplacian-features-for-graph#ran","syntology_url":"https://syntology.ai/paper/2202.06854","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.06854"}},"official":{"repos":["ydtydr/hyla"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/source-free-progressive-graph-learning-for","slug":"source-free-progressive-graph-learning-for","title":"Source-Free Progressive Graph Learning for Open-Set Domain Adaptation","date":"2022-02-13","arxiv_id":"2202.06174","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/source-free-progressive-graph-learning-for#ran","syntology_url":"https://syntology.ai/paper/2202.06174","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.06174"}},"official":{"repos":["BUserName/PGL","luoyadan/sf-pgl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/invariance-principle-meets-out-of","slug":"invariance-principle-meets-out-of","title":"Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs","date":"2022-02-11","arxiv_id":"2202.05441","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/invariance-principle-meets-out-of#ran","syntology_url":"https://syntology.ai/paper/2202.05441","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.05441"}},"official":{"repos":["lfhase/ciga"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/convolutional-neural-networks-on-graphs-with-1","slug":"convolutional-neural-networks-on-graphs-with-1","title":"Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited","date":"2022-02-04","arxiv_id":"2202.03580","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/convolutional-neural-networks-on-graphs-with-1#ran","syntology_url":"https://syntology.ai/paper/2202.03580","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.03580"}},"official":{"repos":["ivam-he/chebnetii"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/interpretable-and-generalizable-graph","slug":"interpretable-and-generalizable-graph","title":"Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism","date":"2022-01-31","arxiv_id":"2201.12987","repositories_listed":2,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":5,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/interpretable-and-generalizable-graph#ran","syntology_url":"https://syntology.ai/paper/2201.12987","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.12987"}},"official":{"repos":["Graph-COM/GSAT"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-approximation-of-extended-persistent","slug":"neural-approximation-of-extended-persistent","title":"Neural Approximation of Graph Topological Features","date":"2022-01-28","arxiv_id":"2201.12032","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/neural-approximation-of-extended-persistent#ran","syntology_url":"https://syntology.ai/paper/2201.12032","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.12032"}},"official":{"repos":["pkuyzy/TLC-GNN"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/gcod-graph-convolutional-network-acceleration","slug":"gcod-graph-convolutional-network-acceleration","title":"GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design","date":"2021-12-22","arxiv_id":"2112.11594","repositories_listed":1,"syntology":{"n":8,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/gcod-graph-convolutional-network-acceleration#ran","syntology_url":"https://syntology.ai/paper/2112.11594","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.11594"}},"official":{"repos":["rice-eic/gcod"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/large-scale-learning-on-non-homophilous","slug":"large-scale-learning-on-non-homophilous","title":"Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods","date":"2021-10-27","arxiv_id":"2110.14446","repositories_listed":5,"syntology":{"n":21,"n_ran":15,"n_constructed":5,"n_ran_checked":15,"n_instrument":0,"n_unverified":6,"n_honours":1,"n_violates":0,"n_no_contract":14,"n_pointer_only":11,"phrase":"15 ran (of which 5 constructed an object rather than computing a result; 15 with no instrument failure: 1 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/large-scale-learning-on-non-homophilous#ran","syntology_url":"https://syntology.ai/paper/2110.14446","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.14446"}},"official":{"repos":["cuai/non-homophily-large-scale"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/towards-open-world-feature-extrapolation-an","slug":"towards-open-world-feature-extrapolation-an","title":"Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach","date":"2021-10-09","arxiv_id":"2110.04514","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/towards-open-world-feature-extrapolation-an#ran","syntology_url":"https://syntology.ai/paper/2110.04514","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.04514"}},"official":{"repos":["qitianwu/FATE"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/dynamic-attentive-graph-learning-for-image","slug":"dynamic-attentive-graph-learning-for-image","title":"Dynamic Attentive Graph Learning for Image Restoration","date":"2021-09-14","arxiv_id":"2109.06620","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dynamic-attentive-graph-learning-for-image#ran","syntology_url":"https://syntology.ai/paper/2109.06620","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.06620"}},"official":{"repos":["jianzhangcs/dagl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/edge-augmented-graph-transformers-global-self","slug":"edge-augmented-graph-transformers-global-self","title":"Global Self-Attention as a Replacement for Graph Convolution","date":"2021-08-07","arxiv_id":"2108.03348","repositories_listed":3,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/edge-augmented-graph-transformers-global-self#ran","syntology_url":"https://syntology.ai/paper/2108.03348","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.03348"}},"official":{"repos":["shamim-hussain/egt","shamim-hussain/egt_pytorch","shamim-hussain/egt_triangular"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/grand-graph-neural-diffusion","slug":"grand-graph-neural-diffusion","title":"GRAND: Graph Neural Diffusion","date":"2021-06-21","arxiv_id":"2106.10934","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/grand-graph-neural-diffusion#ran","syntology_url":"https://syntology.ai/paper/2106.10934","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.10934"}},"official":{"repos":["twitter-research/graph-neural-pde"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-neural-networks-with-local-graph","slug":"graph-neural-networks-with-local-graph","title":"Graph Neural Networks with Local Graph Parameters","date":"2021-06-12","arxiv_id":"2106.06707","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/graph-neural-networks-with-local-graph#ran","syntology_url":"https://syntology.ai/paper/2106.06707","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.06707"}},"official":{"repos":["LGP-GNN-2021/LGP-GNN"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/breaking-the-limit-of-graph-neural-networks","slug":"breaking-the-limit-of-graph-neural-networks","title":"Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns","date":"2021-06-11","arxiv_id":"2106.06586","repositories_listed":1,"syntology":{"n":12,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/breaking-the-limit-of-graph-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2106.06586","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.06586"}},"official":{"repos":["susheels/gnns-and-local-assortativity"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-graph-learning-with","slug":"self-supervised-graph-learning-with","title":"Self-Supervised Graph Learning with Hyperbolic Embedding for Temporal Health Event Prediction","date":"2021-06-09","arxiv_id":"2106.04751","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/self-supervised-graph-learning-with#ran","syntology_url":"https://syntology.ai/paper/2106.04751","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.04751"}},"official":{"repos":["LuChang-CS/sherbet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/counterfactual-graph-learning-for-link","slug":"counterfactual-graph-learning-for-link","title":"Learning from Counterfactual Links for Link Prediction","date":"2021-06-03","arxiv_id":"2106.02172","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/counterfactual-graph-learning-for-link#ran","syntology_url":"https://syntology.ai/paper/2106.02172","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.02172"}},"official":{"repos":["DM2-ND/CFLP"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/collaborative-graph-learning-with-auxiliary","slug":"collaborative-graph-learning-with-auxiliary","title":"Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare","date":"2021-05-16","arxiv_id":"2105.07542","repositories_listed":1,"syntology":{"n":16,"n_ran":14,"n_constructed":8,"n_ran_checked":14,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":2,"n_no_contract":11,"n_pointer_only":0,"phrase":"14 ran (of which 8 constructed an object rather than computing a result; 14 with no instrument failure: 1 honoured, 2 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/collaborative-graph-learning-with-auxiliary#ran","syntology_url":"https://syntology.ai/paper/2105.07542","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.07542"}},"official":{"repos":["LuChang-CS/CGL"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":8,"n_ran_no_instrument_failure":14,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/consistency-of-mechanistic-causal-discovery","slug":"consistency-of-mechanistic-causal-discovery","title":"Neural graphical modelling in continuous-time: consistency guarantees and algorithms","date":"2021-05-06","arxiv_id":"2105.02522","repositories_listed":2,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/consistency-of-mechanistic-causal-discovery#ran","syntology_url":"https://syntology.ai/paper/2105.02522","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.02522"}},"official":{"repos":["vanderschaarlab/mlforhealthlabpub"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/autogl-a-library-for-automated-graph-learning","slug":"autogl-a-library-for-automated-graph-learning","title":"AutoGL: A Library for Automated Graph Learning","date":"2021-04-11","arxiv_id":"2104.04987","repositories_listed":2,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/autogl-a-library-for-automated-graph-learning#ran","syntology_url":"https://syntology.ai/paper/2104.04987","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.04987"}},"official":{"repos":["THUMNLab/AutoGL","thumnlab/autogl-light"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/mutual-graph-learning-for-camouflaged-object","slug":"mutual-graph-learning-for-camouflaged-object","title":"Mutual Graph Learning for Camouflaged Object Detection","date":"2021-04-03","arxiv_id":"2104.02613","repositories_listed":1,"syntology":{"n":13,"n_ran":7,"n_constructed":0,"n_ran_checked":4,"n_instrument":3,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":13,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/mutual-graph-learning-for-camouflaged-object#ran","syntology_url":"https://syntology.ai/paper/2104.02613","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.02613"}},"official":{"repos":["fanyang587/MGL"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/graphsmote-imbalanced-node-classification-on","slug":"graphsmote-imbalanced-node-classification-on","title":"GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks","date":"2021-03-16","arxiv_id":"2103.08826","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/graphsmote-imbalanced-node-classification-on#ran","syntology_url":"https://syntology.ai/paper/2103.08826","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.08826"}},"official":{"repos":["TianxiangZhao/GraphSmote"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/accurate-learning-of-graph-representations-1","slug":"accurate-learning-of-graph-representations-1","title":"Accurate Learning of Graph Representations with Graph Multiset Pooling","date":"2021-02-23","arxiv_id":"2102.11533","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":4,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"4 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","sample_list":"/paper/accurate-learning-of-graph-representations-1#ran","syntology_url":"https://syntology.ai/paper/2102.11533","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.11533"}},"official":{"repos":["JinheonBaek/GMT"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/persistence-homology-for-link-prediction-an","slug":"persistence-homology-for-link-prediction-an","title":"Link Prediction with Persistent Homology: An Interactive View","date":"2021-02-20","arxiv_id":"2102.10255","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/persistence-homology-for-link-prediction-an#ran","syntology_url":"https://syntology.ai/paper/2102.10255","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.10255"}},"official":{"repos":["pkuyzy/TLC-GNN"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-on-attribute-missing-graphs","slug":"learning-on-attribute-missing-graphs","title":"Learning on Attribute-Missing Graphs","date":"2020-11-03","arxiv_id":"2011.01623","repositories_listed":3,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-on-attribute-missing-graphs#ran","syntology_url":"https://syntology.ai/paper/2011.01623","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.01623"}},"official":{"repos":["xuChenSJTU/SAT-master-online"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/graph-information-bottleneck-for-subgraph-1","slug":"graph-information-bottleneck-for-subgraph-1","title":"Graph Information Bottleneck for Subgraph Recognition","date":"2020-10-12","arxiv_id":"2010.05563","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/graph-information-bottleneck-for-subgraph-1#ran","syntology_url":"https://syntology.ai/paper/2010.05563","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.05563"}},"official":{"repos":["Samyu0304/graph-information-bottleneck-for-Subgraph-Recognition"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/implicit-graph-neural-networks","slug":"implicit-graph-neural-networks","title":"Implicit Graph Neural Networks","date":"2020-09-14","arxiv_id":"2009.06211","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":2,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/implicit-graph-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2009.06211","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.06211"}},"official":{"repos":["SwiftieH/IGNN"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/lifelong-graph-learning","slug":"lifelong-graph-learning","title":"Lifelong Graph Learning","date":"2020-09-01","arxiv_id":"2009.00647","repositories_listed":3,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/lifelong-graph-learning#ran","syntology_url":"https://syntology.ai/paper/2009.00647","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.00647"}},"official":{"repos":["wang-chen/LGL","wang-chen/lgl-action-recognition"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/adversarial-bipartite-graph-learning-for","slug":"adversarial-bipartite-graph-learning-for","title":"Adversarial Bipartite Graph Learning for Video Domain Adaptation","date":"2020-07-31","arxiv_id":"2007.15829","repositories_listed":1,"syntology":{"n":9,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/adversarial-bipartite-graph-learning-for#ran","syntology_url":"https://syntology.ai/paper/2007.15829","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.15829"}},"official":{"repos":["Luoyadan/MM2020_ABG"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-convolutional-networks-for-graphs","slug":"graph-convolutional-networks-for-graphs","title":"Graph Convolutional Networks for Graphs Containing Missing Features","date":"2020-07-09","arxiv_id":"2007.04583","repositories_listed":2,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/graph-convolutional-networks-for-graphs#ran","syntology_url":"https://syntology.ai/paper/2007.04583","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.04583"}},"official":{"repos":["marblet/GCNmf"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/progressive-graph-learning-for-open-set","slug":"progressive-graph-learning-for-open-set","title":"Progressive Graph Learning for Open-Set Domain Adaptation","date":"2020-06-22","arxiv_id":"2006.12087","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/progressive-graph-learning-for-open-set#ran","syntology_url":"https://syntology.ai/paper/2006.12087","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.12087"}},"official":{"repos":["BUserName/PGL"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/iterative-deep-graph-learning-for-graph-1","slug":"iterative-deep-graph-learning-for-graph-1","title":"Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings","date":"2020-06-21","arxiv_id":"2006.13009","repositories_listed":2,"syntology":{"n":14,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":6,"n_honours":1,"n_violates":1,"n_no_contract":5,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 1 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/iterative-deep-graph-learning-for-graph-1#ran","syntology_url":"https://syntology.ai/paper/2006.13009","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.13009"}},"official":{"repos":["hugochan/IDGL"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/gcc-graph-contrastive-coding-for-graph-neural","slug":"gcc-graph-contrastive-coding-for-graph-neural","title":"GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training","date":"2020-06-17","arxiv_id":"2006.09963","repositories_listed":4,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/gcc-graph-contrastive-coding-for-graph-neural#ran","syntology_url":"https://syntology.ai/paper/2006.09963","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.09963"}},"official":{"repos":["THUDM/GCC"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/connecting-the-dots-multivariate-time-series","slug":"connecting-the-dots-multivariate-time-series","title":"Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks","date":"2020-05-24","arxiv_id":"2005.11650","repositories_listed":3,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/connecting-the-dots-multivariate-time-series#ran","syntology_url":"https://syntology.ai/paper/2005.11650","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.11650"}},"official":{"repos":["nnzhan/MTGNN"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/graph-random-neural-network","slug":"graph-random-neural-network","title":"Graph Random Neural Network for Semi-Supervised Learning on Graphs","date":"2020-05-22","arxiv_id":"2005.11079","repositories_listed":9,"syntology":{"n":22,"n_ran":15,"n_constructed":1,"n_ran_checked":9,"n_instrument":6,"n_unverified":7,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"15 ran (of which 1 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 6 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/graph-random-neural-network#ran","syntology_url":"https://syntology.ai/paper/2005.11079","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.11079"}},"official":{"repos":["Grand20/grand"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/understanding-negative-sampling-in-graph","slug":"understanding-negative-sampling-in-graph","title":"Understanding Negative Sampling in Graph Representation Learning","date":"2020-05-20","arxiv_id":"2005.09863","repositories_listed":4,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/understanding-negative-sampling-in-graph#ran","syntology_url":"https://syntology.ai/paper/2005.09863","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.09863"}},"official":{"repos":["zyang-16/MCNS"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/graph-based-self-supervised-program-repair","slug":"graph-based-self-supervised-program-repair","title":"Graph-based, Self-Supervised Program Repair from Diagnostic Feedback","date":"2020-05-20","arxiv_id":"2005.10636","repositories_listed":2,"syntology":{"n":6,"n_ran":4,"n_constructed":2,"n_ran_checked":3,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/graph-based-self-supervised-program-repair#ran","syntology_url":"https://syntology.ai/paper/2005.10636","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.10636"}},"official":{"repos":["michiyasunaga/DrRepair","worksheets.codalab.org/worksheets/0x01838644724a433c932bef4cb5c42fbd"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/hypergraph-learning-with-line-expansion","slug":"hypergraph-learning-with-line-expansion","title":"Semi-supervised Hypergraph Node Classification on Hypergraph Line Expansion","date":"2020-05-11","arxiv_id":"2005.04843","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hypergraph-learning-with-line-expansion#ran","syntology_url":"https://syntology.ai/paper/2005.04843","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.04843"}},"official":{"repos":["ycq091044/legcn"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/pick-processing-key-information-extraction","slug":"pick-processing-key-information-extraction","title":"PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks","date":"2020-04-16","arxiv_id":"2004.07464","repositories_listed":2,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pick-processing-key-information-extraction#ran","syntology_url":"https://syntology.ai/paper/2004.07464","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.07464"}},"official":{"repos":["wenwenyu/PICK-pytorch"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/the-general-theory-of-permutation-equivarant","slug":"the-general-theory-of-permutation-equivarant","title":"The general theory of permutation equivarant neural networks and higher order graph variational encoders","date":"2020-04-08","arxiv_id":"2004.03990","repositories_listed":1,"syntology":{"n":17,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/the-general-theory-of-permutation-equivarant#ran","syntology_url":"https://syntology.ai/paper/2004.03990","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.03990"}},"official":null}},{"url":"/paper/automating-botnet-detection-with-graph-neural","slug":"automating-botnet-detection-with-graph-neural","title":"Automating Botnet Detection with Graph Neural Networks","date":"2020-03-13","arxiv_id":"2003.06344","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/automating-botnet-detection-with-graph-neural#ran","syntology_url":"https://syntology.ai/paper/2003.06344","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.06344"}},"official":{"repos":["harvardnlp/botnet-detection"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-universal-adversarial-attacks-a-few-bad","slug":"graph-universal-adversarial-attacks-a-few-bad","title":"Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models","date":"2020-02-12","arxiv_id":"2002.04784","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/graph-universal-adversarial-attacks-a-few-bad#ran","syntology_url":"https://syntology.ai/paper/2002.04784","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.04784"}},"official":{"repos":["chisam0217/Graph-Universal-Attack"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-subgraph-isomorphism-counting-1","slug":"neural-subgraph-isomorphism-counting-1","title":"Neural Subgraph Isomorphism Counting","date":"2019-12-25","arxiv_id":"1912.11589","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/neural-subgraph-isomorphism-counting-1#ran","syntology_url":"https://syntology.ai/paper/1912.11589","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.11589"}},"official":{"repos":["HKUST-KnowComp/NeuralSubgraphCounting"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-graph-library-towards-efficient-and","slug":"deep-graph-library-towards-efficient-and","title":"Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks","date":"2019-09-03","arxiv_id":"1909.01315","repositories_listed":7,"syntology":{"n":39,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":28,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":4,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 28 unverified","sample_list":"/paper/deep-graph-library-towards-efficient-and#ran","syntology_url":"https://syntology.ai/paper/1909.01315","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.01315"}},"official":{"repos":["dglai/dgl-0.5-benchmark"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/provably-powerful-graph-networks","slug":"provably-powerful-graph-networks","title":"Provably Powerful Graph Networks","date":"2019-05-27","arxiv_id":"1905.11136","repositories_listed":2,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/provably-powerful-graph-networks#ran","syntology_url":"https://syntology.ai/paper/1905.11136","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.11136"}},"official":null}},{"url":"/paper/multi-stage-self-supervised-learning-for","slug":"multi-stage-self-supervised-learning-for","title":"Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels","date":"2019-02-28","arxiv_id":"1902.11038","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multi-stage-self-supervised-learning-for#ran","syntology_url":"https://syntology.ai/paper/1902.11038","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.11038"}},"official":null}},{"url":"/paper/how-to-learn-a-graph-from-smooth-signals","slug":"how-to-learn-a-graph-from-smooth-signals","title":"How to learn a graph from smooth signals","date":"2016-01-11","arxiv_id":"1601.02513","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/how-to-learn-a-graph-from-smooth-signals#ran","syntology_url":"https://syntology.ai/paper/1601.02513","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1601.02513"}},"official":null}}],"record_sha256":"99663eb0b546392ed9ac247dcbdc5fc4dfb85bd65a46feb04c5cf52469092609","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}