{"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/node-classification/papers/4","list_of":"/task/node-classification","task":"Node Classification","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":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":4,"pages_in_order":19,"rows_per_page":100,"rows":[301,400],"of":1860,"counts":{"archive_papers_tagged":1860,"with_a_code_link":991,"where_syntology_ran_a_sample":305,"not_listed_spam_title":0,"listed":1860,"listed_where_code_ran":305,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":256,"every_run_a_failure_of_syntologys_instrument":49,"listed_with_a_run_with_no_instrument_failure":256,"listed_every_run_a_failure_of_syntologys_instrument":49,"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/node-classification","prev":"/task/node-classification/papers/3","next":"/task/node-classification/papers/5","papers":[{"url":"/paper/a-pure-transformer-pretraining-framework-on","slug":"a-pure-transformer-pretraining-framework-on","title":"A Pure Transformer Pretraining Framework on Text-attributed Graphs","date":"2024-06-19","arxiv_id":"2406.13873","repositories_listed":1,"syntology":null},{"url":"/paper/a-data-centric-approach-for-assessing","slug":"a-data-centric-approach-for-assessing","title":"A data-centric approach for assessing progress of Graph Neural Networks","date":"2024-06-18","arxiv_id":"2406.12439","repositories_listed":1,"syntology":null},{"url":"/paper/the-heterophilic-snowflake-hypothesis","slug":"the-heterophilic-snowflake-hypothesis","title":"The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs","date":"2024-06-18","arxiv_id":"2406.12539","repositories_listed":1,"syntology":null},{"url":"/paper/edge-classification-on-graphs-new-directions","slug":"edge-classification-on-graphs-new-directions","title":"Edge Classification on Graphs: New Directions in Topological Imbalance","date":"2024-06-17","arxiv_id":"2406.11685","repositories_listed":1,"syntology":null},{"url":"/paper/graph-knowledge-distillation-to-mixture-of","slug":"graph-knowledge-distillation-to-mixture-of","title":"Graph Knowledge Distillation to Mixture of Experts","date":"2024-06-17","arxiv_id":"2406.11919","repositories_listed":1,"syntology":null},{"url":"/paper/pown-prototypical-open-world-node","slug":"pown-prototypical-open-world-node","title":"POWN: Prototypical Open-World Node Classification","date":"2024-06-14","arxiv_id":"2406.09926","repositories_listed":1,"syntology":null},{"url":"/paper/a-comprehensive-graph-pooling-benchmark","slug":"a-comprehensive-graph-pooling-benchmark","title":"A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability","date":"2024-06-13","arxiv_id":"2406.09031","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":11,"phrase":"10 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-comprehensive-graph-pooling-benchmark#ran","syntology_url":"https://syntology.ai/paper/2406.09031","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.09031"}},"official":{"repos":["goose315/graph_pooling_benchmark"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/classic-gnns-are-strong-baselines-reassessing","slug":"classic-gnns-are-strong-baselines-reassessing","title":"Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification","date":"2024-06-13","arxiv_id":"2406.08993","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":3,"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) · 4 unverified","sample_list":"/paper/classic-gnns-are-strong-baselines-reassessing#ran","syntology_url":"https://syntology.ai/paper/2406.08993","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.08993"}},"official":{"repos":["LUOyk1999/tunedGNN"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-topology-aware-data-augmentation","slug":"efficient-topology-aware-data-augmentation","title":"Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks","date":"2024-06-08","arxiv_id":"2406.05482","repositories_listed":1,"syntology":null},{"url":"/paper/transfer-entropy-in-graph-convolutional","slug":"transfer-entropy-in-graph-convolutional","title":"Transfer Entropy in Graph Convolutional Neural Networks","date":"2024-06-08","arxiv_id":"2406.06632","repositories_listed":1,"syntology":null},{"url":"/paper/cooperative-meta-learning-with-gradient","slug":"cooperative-meta-learning-with-gradient","title":"Cooperative Meta-Learning with Gradient Augmentation","date":"2024-06-07","arxiv_id":"2406.04639","repositories_listed":1,"syntology":null},{"url":"/paper/linkgpt-teaching-large-language-models-to","slug":"linkgpt-teaching-large-language-models-to","title":"LinkGPT: Teaching Large Language Models To Predict Missing Links","date":"2024-06-07","arxiv_id":"2406.04640","repositories_listed":1,"syntology":null},{"url":"/paper/linear-opinion-pooling-for-uncertainty","slug":"linear-opinion-pooling-for-uncertainty","title":"Linear Opinion Pooling for Uncertainty Quantification on Graphs","date":"2024-06-06","arxiv_id":"2406.04041","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/linear-opinion-pooling-for-uncertainty#ran","syntology_url":"https://syntology.ai/paper/2406.04041","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.04041"}},"official":{"repos":["cortys/gpn-extensions"],"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/panda-expanded-width-aware-message-passing","slug":"panda-expanded-width-aware-message-passing","title":"PANDA: Expanded Width-Aware Message Passing Beyond Rewiring","date":"2024-06-06","arxiv_id":"2406.03671","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"7 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/panda-expanded-width-aware-message-passing#ran","syntology_url":"https://syntology.ai/paper/2406.03671","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.03671"}},"official":{"repos":["jeongwhanchoi/panda"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-long-range-dependencies-on-graphs","slug":"learning-long-range-dependencies-on-graphs","title":"Learning Long Range Dependencies on Graphs via Random Walks","date":"2024-06-05","arxiv_id":"2406.03386","repositories_listed":1,"syntology":{"n":13,"n_ran":13,"n_constructed":0,"n_ran_checked":10,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":1,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-long-range-dependencies-on-graphs#ran","syntology_url":"https://syntology.ai/paper/2406.03386","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.03386"}},"official":{"repos":["borgwardtlab/neuralwalker"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/agale-a-graph-aware-continual-learning","slug":"agale-a-graph-aware-continual-learning","title":"AGALE: A Graph-Aware Continual Learning Evaluation Framework","date":"2024-06-03","arxiv_id":"2406.01229","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-solve-multiresolution-matrix","slug":"learning-to-solve-multiresolution-matrix","title":"Learning to Solve Multiresolution Matrix Factorization by Manifold Optimization and Evolutionary Metaheuristics","date":"2024-06-01","arxiv_id":"2406.00469","repositories_listed":1,"syntology":null},{"url":"/paper/learning-on-large-graphs-using-intersecting","slug":"learning-on-large-graphs-using-intersecting","title":"Learning on Large Graphs using Intersecting Communities","date":"2024-05-31","arxiv_id":"2405.20724","repositories_listed":1,"syntology":null},{"url":"/paper/sign-is-not-a-remedy-multiset-to-multiset","slug":"sign-is-not-a-remedy-multiset-to-multiset","title":"Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs","date":"2024-05-31","arxiv_id":"2405.20652","repositories_listed":1,"syntology":null},{"url":"/paper/towards-a-general-gnn-framework-for","slug":"towards-a-general-gnn-framework-for","title":"Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNs","date":"2024-05-31","arxiv_id":"2405.20543","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":13,"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) · 3 unverified","sample_list":"/paper/towards-a-general-gnn-framework-for#ran","syntology_url":"https://syntology.ai/paper/2405.20543","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.20543"}},"official":{"repos":["wenkelf/copt"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/graphany-a-foundation-model-for-node","slug":"graphany-a-foundation-model-for-node","title":"Fully-inductive Node Classification on Arbitrary Graphs","date":"2024-05-30","arxiv_id":"2405.20445","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/graphany-a-foundation-model-for-node#ran","syntology_url":"https://syntology.ai/paper/2405.20445","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.20445"}},"official":{"repos":["deepgraphlearning/graphany"],"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/spatio-spectral-graph-neural-networks","slug":"spatio-spectral-graph-neural-networks","title":"Spatio-Spectral Graph Neural Networks","date":"2024-05-29","arxiv_id":"2405.19121","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/spatio-spectral-graph-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2405.19121","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.19121"}},"official":{"repos":["sigeisler/s2gnn"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/structure-aware-semantic-node-identifiers-for","slug":"structure-aware-semantic-node-identifiers-for","title":"Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning","date":"2024-05-26","arxiv_id":"2405.16435","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":2,"n_no_contract":7,"n_pointer_only":3,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 2 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/structure-aware-semantic-node-identifiers-for#ran","syntology_url":"https://syntology.ai/paper/2405.16435","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.16435"}},"official":{"repos":["LUOyk1999/NodeID"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/ags-gnn-attribute-guided-sampling-for-graph","slug":"ags-gnn-attribute-guided-sampling-for-graph","title":"AGS-GNN: Attribute-guided Sampling for Graph Neural Networks","date":"2024-05-24","arxiv_id":"2405.15218","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-independent-cross-entropy-loss-for","slug":"rethinking-independent-cross-entropy-loss-for","title":"Rethinking Independent Cross-Entropy Loss For Graph-Structured Data","date":"2024-05-24","arxiv_id":"2405.15564","repositories_listed":1,"syntology":null},{"url":"/paper/similarity-navigated-conformal-prediction-for","slug":"similarity-navigated-conformal-prediction-for","title":"Similarity-Navigated Conformal Prediction for Graph Neural Networks","date":"2024-05-23","arxiv_id":"2405.14303","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":2,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"6 ran (of which 2 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/similarity-navigated-conformal-prediction-for#ran","syntology_url":"https://syntology.ai/paper/2405.14303","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.14303"}},"official":{"repos":["janqsong/snaps"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":2,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/login-a-large-language-model-consulted-graph","slug":"login-a-large-language-model-consulted-graph","title":"LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework","date":"2024-05-22","arxiv_id":"2405.13902","repositories_listed":1,"syntology":null},{"url":"/paper/utilizing-description-logics-for-global","slug":"utilizing-description-logics-for-global","title":"Utilizing Description Logics for Global Explanations of Heterogeneous Graph Neural Networks","date":"2024-05-21","arxiv_id":"2405.12654","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":14,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/utilizing-description-logics-for-global#ran","syntology_url":"https://syntology.ai/paper/2405.12654","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.12654"}},"official":{"repos":["ds-jrg/xgnn-dl"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/perception-inspired-graph-convolution-for","slug":"perception-inspired-graph-convolution-for","title":"Perception-Inspired Graph Convolution for Music Understanding Tasks","date":"2024-05-15","arxiv_id":"2405.09224","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/perception-inspired-graph-convolution-for#ran","syntology_url":"https://syntology.ai/paper/2405.09224","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.09224"}},"official":{"repos":["manoskary/musgconv"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/a-survey-of-large-language-models-for-graphs","slug":"a-survey-of-large-language-models-for-graphs","title":"A Survey of Large Language Models for Graphs","date":"2024-05-10","arxiv_id":"2405.08011","repositories_listed":1,"syntology":null},{"url":"/paper/coefficient-decomposition-for-spectral-graph","slug":"coefficient-decomposition-for-spectral-graph","title":"Coefficient Decomposition for Spectral Graph Convolution","date":"2024-05-06","arxiv_id":"2405.03296","repositories_listed":1,"syntology":null},{"url":"/paper/slotgat-slot-based-message-passing-for","slug":"slotgat-slot-based-message-passing-for","title":"SlotGAT: Slot-based Message Passing for Heterogeneous Graph Neural Network","date":"2024-05-03","arxiv_id":"2405.01927","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/slotgat-slot-based-message-passing-for#ran","syntology_url":"https://syntology.ai/paper/2405.01927","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.01927"}},"official":{"repos":["scottjiao/slotgat_icml23"],"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/intramix-intra-class-mixup-generation-for","slug":"intramix-intra-class-mixup-generation-for","title":"IntraMix: Intra-Class Mixup Generation for Accurate Labels and Neighbors","date":"2024-05-02","arxiv_id":"2405.00957","repositories_listed":1,"syntology":null},{"url":"/paper/lying-graph-convolution-learning-to-lie-for","slug":"lying-graph-convolution-learning-to-lie-for","title":"Lying Graph Convolution: Learning to Lie for Node Classification Tasks","date":"2024-05-02","arxiv_id":"2405.01247","repositories_listed":1,"syntology":null},{"url":"/paper/training-free-graph-neural-networks-and-the","slug":"training-free-graph-neural-networks-and-the","title":"Training-free Graph Neural Networks and the Power of Labels as Features","date":"2024-04-30","arxiv_id":"2404.19288","repositories_listed":1,"syntology":null},{"url":"/paper/are-graph-embeddings-the-panacea-an-empirical","slug":"are-graph-embeddings-the-panacea-an-empirical","title":"Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness Perspective","date":"2024-04-25","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ckgconv-general-graph-convolution-with","slug":"ckgconv-general-graph-convolution-with","title":"CKGConv: General Graph Convolution with Continuous Kernels","date":"2024-04-21","arxiv_id":"2404.13604","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":11,"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) · 3 unverified","sample_list":"/paper/ckgconv-general-graph-convolution-with#ran","syntology_url":"https://syntology.ai/paper/2404.13604","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.13604"}},"official":{"repos":["networkslab/ckgconv"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/elevating-spectral-gnns-through-enhanced-band","slug":"elevating-spectral-gnns-through-enhanced-band","title":"Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices Approach","date":"2024-04-15","arxiv_id":"2404.15354","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-attention-models-for-multi","slug":"hierarchical-attention-models-for-multi","title":"Hierarchical Attention Models for Multi-Relational Graphs","date":"2024-04-14","arxiv_id":"2404.09365","repositories_listed":1,"syntology":null},{"url":"/paper/videosage-video-summarization-with-graph","slug":"videosage-video-summarization-with-graph","title":"VideoSAGE: Video Summarization with Graph Representation Learning","date":"2024-04-14","arxiv_id":"2404.10539","repositories_listed":1,"syntology":null},{"url":"/paper/spectral-graph-pruning-against-over-squashing","slug":"spectral-graph-pruning-against-over-squashing","title":"Spectral Graph Pruning Against Over-Squashing and Over-Smoothing","date":"2024-04-06","arxiv_id":"2404.04612","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/spectral-graph-pruning-against-over-squashing#ran","syntology_url":"https://syntology.ai/paper/2404.04612","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.04612"}},"official":{"repos":["relationalml/spectralpruningbraess"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/theoretical-and-empirical-insights-into-the","slug":"theoretical-and-empirical-insights-into-the","title":"Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks","date":"2024-04-04","arxiv_id":"2404.03139","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/theoretical-and-empirical-insights-into-the#ran","syntology_url":"https://syntology.ai/paper/2404.03139","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.03139"}},"official":{"repos":["arjunsubramonian/degree-bias-exploration"],"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/generative-contrastive-heterogeneous-graph","slug":"generative-contrastive-heterogeneous-graph","title":"Generative-Contrastive Heterogeneous Graph Neural Network","date":"2024-04-03","arxiv_id":"2404.02810","repositories_listed":1,"syntology":null},{"url":"/paper/glemos-benchmark-for-instantaneous-graph-1","slug":"glemos-benchmark-for-instantaneous-graph-1","title":"GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection","date":"2024-04-02","arxiv_id":"2404.01578","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/glemos-benchmark-for-instantaneous-graph-1#ran","syntology_url":"https://syntology.ai/paper/2404.01578","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.01578"}},"official":{"repos":["facebookresearch/glemos"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/hypeboy-generative-self-supervised","slug":"hypeboy-generative-self-supervised","title":"HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs","date":"2024-03-31","arxiv_id":"2404.00638","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":5,"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/hypeboy-generative-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2404.00638","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.00638"}},"official":{"repos":["kswoo97/hypeboy"],"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/healthgat-node-classifications-in-electronic","slug":"healthgat-node-classifications-in-electronic","title":"HealthGAT: Node Classifications in Electronic Health Records using Graph Attention Networks","date":"2024-03-26","arxiv_id":"2403.18128","repositories_listed":1,"syntology":null},{"url":"/paper/learn-from-heterophily-heterophilous","slug":"learn-from-heterophily-heterophilous","title":"Learn from Heterophily: Heterophilous Information-enhanced Graph Neural Network","date":"2024-03-26","arxiv_id":"2403.17351","repositories_listed":1,"syntology":null},{"url":"/paper/open-world-semi-supervised-learning-for-node","slug":"open-world-semi-supervised-learning-for-node","title":"Open-World Semi-Supervised Learning for Node Classification","date":"2024-03-18","arxiv_id":"2403.11483","repositories_listed":1,"syntology":null},{"url":"/paper/l-2-gc-lorentzian-linear-graph-convolutional","slug":"l-2-gc-lorentzian-linear-graph-convolutional","title":"L^2GC:Lorentzian Linear Graph Convolutional Networks for Node Classification","date":"2024-03-10","arxiv_id":"2403.06064","repositories_listed":1,"syntology":null},{"url":"/paper/task-oriented-gnns-training-on-large","slug":"task-oriented-gnns-training-on-large","title":"Task-Oriented GNNs Training on Large Knowledge Graphs for Accurate and Efficient Modeling","date":"2024-03-09","arxiv_id":"2403.05752","repositories_listed":1,"syntology":null},{"url":"/paper/spectral-invariant-learning-for-dynamic-1","slug":"spectral-invariant-learning-for-dynamic-1","title":"Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts","date":"2024-03-08","arxiv_id":"2403.05026","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":8,"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/spectral-invariant-learning-for-dynamic-1#ran","syntology_url":"https://syntology.ai/paper/2403.05026","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.05026"}},"official":{"repos":["wondergo2017/sild"],"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/entropy-aware-message-passing-in-graph-neural","slug":"entropy-aware-message-passing-in-graph-neural","title":"Entropy Aware Message Passing in Graph Neural Networks","date":"2024-03-07","arxiv_id":"2403.04636","repositories_listed":1,"syntology":null},{"url":"/paper/opengraph-towards-open-graph-foundation","slug":"opengraph-towards-open-graph-foundation","title":"OpenGraph: Towards Open Graph Foundation Models","date":"2024-03-02","arxiv_id":"2403.01121","repositories_listed":1,"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/opengraph-towards-open-graph-foundation#ran","syntology_url":"https://syntology.ai/paper/2403.01121","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.01121"}},"official":{"repos":["hkuds/opengraph"],"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/pairwise-alignment-improves-graph-domain","slug":"pairwise-alignment-improves-graph-domain","title":"Pairwise Alignment Improves Graph Domain Adaptation","date":"2024-03-02","arxiv_id":"2403.01092","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":0,"n_instrument":5,"n_unverified":0,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 5 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pairwise-alignment-improves-graph-domain#ran","syntology_url":"https://syntology.ai/paper/2403.01092","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.01092"}},"official":{"repos":["graph-com/pair-align"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fedstruct-federated-decoupled-learning-over","slug":"fedstruct-federated-decoupled-learning-over","title":"Decoupled Subgraph Federated Learning","date":"2024-02-29","arxiv_id":"2402.19163","repositories_listed":1,"syntology":null},{"url":"/paper/a-simple-and-yet-fairly-effective-defense-for","slug":"a-simple-and-yet-fairly-effective-defense-for","title":"A Simple and Yet Fairly Effective Defense for Graph Neural Networks","date":"2024-02-21","arxiv_id":"2402.13987","repositories_listed":1,"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":1,"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/a-simple-and-yet-fairly-effective-defense-for#ran","syntology_url":"https://syntology.ai/paper/2402.13987","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.13987"}},"official":{"repos":["sennadir/noisygnn"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/endowing-pre-trained-graph-models-with","slug":"endowing-pre-trained-graph-models-with","title":"Endowing Pre-trained Graph Models with Provable Fairness","date":"2024-02-19","arxiv_id":"2402.12161","repositories_listed":1,"syntology":null},{"url":"/paper/large-language-model-driven-meta-structure","slug":"large-language-model-driven-meta-structure","title":"Large Language Model-driven Meta-structure Discovery in Heterogeneous Information Network","date":"2024-02-18","arxiv_id":"2402.11518","repositories_listed":1,"syntology":{"n":13,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":9,"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) · 9 unverified","sample_list":"/paper/large-language-model-driven-meta-structure#ran","syntology_url":"https://syntology.ai/paper/2402.11518","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.11518"}},"official":{"repos":["linchen-65/restruct"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/masked-attention-is-all-you-need-for-graphs","slug":"masked-attention-is-all-you-need-for-graphs","title":"An end-to-end attention-based approach for learning on graphs","date":"2024-02-16","arxiv_id":"2402.10793","repositories_listed":1,"syntology":null},{"url":"/paper/graph-inference-acceleration-by-learning-mlps","slug":"graph-inference-acceleration-by-learning-mlps","title":"SimMLP: Training MLPs on Graphs without Supervision","date":"2024-02-14","arxiv_id":"2402.08918","repositories_listed":1,"syntology":null},{"url":"/paper/graph-skeleton-1-nodes-are-sufficient-to","slug":"graph-skeleton-1-nodes-are-sufficient-to","title":"Graph-Skeleton: ~1% Nodes are Sufficient to Represent Billion-Scale Graph","date":"2024-02-14","arxiv_id":"2402.09565","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":4,"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/graph-skeleton-1-nodes-are-sufficient-to#ran","syntology_url":"https://syntology.ai/paper/2402.09565","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.09565"}},"official":{"repos":["zjunet/graphskeleton"],"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/grassrep-graph-based-self-supervised-learning","slug":"grassrep-graph-based-self-supervised-learning","title":"GraSSRep: Graph-Based Self-Supervised Learning for Repeat Detection in Metagenomic Assembly","date":"2024-02-14","arxiv_id":"2402.09381","repositories_listed":1,"syntology":null},{"url":"/paper/disambiguated-node-classification-with-graph","slug":"disambiguated-node-classification-with-graph","title":"Disambiguated Node Classification with Graph Neural Networks","date":"2024-02-13","arxiv_id":"2402.08824","repositories_listed":1,"syntology":null},{"url":"/paper/netinfof-framework-measuring-and-exploiting","slug":"netinfof-framework-measuring-and-exploiting","title":"NetInfoF Framework: Measuring and Exploiting Network Usable Information","date":"2024-02-12","arxiv_id":"2402.07999","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/netinfof-framework-measuring-and-exploiting#ran","syntology_url":"https://syntology.ai/paper/2402.07999","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.07999"}},"official":{"repos":["amazon-science/network-usable-info-framework"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/graphtranslator-aligning-graph-model-to-large","slug":"graphtranslator-aligning-graph-model-to-large","title":"GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks","date":"2024-02-11","arxiv_id":"2402.07197","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/graphtranslator-aligning-graph-model-to-large#ran","syntology_url":"https://syntology.ai/paper/2402.07197","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.07197"}},"official":{"repos":["alibaba/graphtranslator"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/hyperbert-mixing-hypergraph-aware-layers-with","slug":"hyperbert-mixing-hypergraph-aware-layers-with","title":"HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed Hypergraphs","date":"2024-02-11","arxiv_id":"2402.07309","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-node-wise-propagation-for-large","slug":"rethinking-node-wise-propagation-for-large","title":"Rethinking Node-wise Propagation for Large-scale Graph Learning","date":"2024-02-09","arxiv_id":"2402.06128","repositories_listed":1,"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":9,"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/rethinking-node-wise-propagation-for-large#ran","syntology_url":"https://syntology.ai/paper/2402.06128","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.06128"}},"official":{"repos":["xkli-allen/atp"],"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/masked-graph-autoencoder-with-non-discrete","slug":"masked-graph-autoencoder-with-non-discrete","title":"Masked Graph Autoencoder with Non-discrete Bandwidths","date":"2024-02-06","arxiv_id":"2402.03814","repositories_listed":1,"syntology":null},{"url":"/paper/similarity-based-neighbor-selection-for-graph","slug":"similarity-based-neighbor-selection-for-graph","title":"Similarity-based Neighbor Selection for Graph LLMs","date":"2024-02-06","arxiv_id":"2402.03720","repositories_listed":1,"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":9,"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/similarity-based-neighbor-selection-for-graph#ran","syntology_url":"https://syntology.ai/paper/2402.03720","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.03720"}},"official":{"repos":["ruili33/sns"],"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/neural-scaling-laws-on-graphs","slug":"neural-scaling-laws-on-graphs","title":"Towards Neural Scaling Laws on Graphs","date":"2024-02-03","arxiv_id":"2402.02054","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"5 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/neural-scaling-laws-on-graphs#ran","syntology_url":"https://syntology.ai/paper/2402.02054","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.02054"}},"official":{"repos":["liu-jingzhe/graph-scaling-laws"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/no-need-to-look-back-an-efficient-and","slug":"no-need-to-look-back-an-efficient-and","title":"Scalable and Efficient Temporal Graph Representation Learning via Forward Recent Sampling","date":"2024-02-03","arxiv_id":"2402.01964","repositories_listed":1,"syntology":null},{"url":"/paper/rendering-graphs-for-graph-reasoning-in","slug":"rendering-graphs-for-graph-reasoning-in","title":"GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning","date":"2024-02-03","arxiv_id":"2402.02130","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":10,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/rendering-graphs-for-graph-reasoning-in#ran","syntology_url":"https://syntology.ai/paper/2402.02130","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.02130"}},"official":{"repos":["WEIYanbin1999/GITA"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/l2g2g-a-scalable-local-to-global-network","slug":"l2g2g-a-scalable-local-to-global-network","title":"L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders","date":"2024-02-02","arxiv_id":"2402.01614","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/l2g2g-a-scalable-local-to-global-network#ran","syntology_url":"https://syntology.ai/paper/2402.01614","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.01614"}},"official":{"repos":["tonyauyeung/local2gae2global"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/igcn-integrative-graph-convolutional-networks","slug":"igcn-integrative-graph-convolutional-networks","title":"IGCN: Integrative Graph Convolution Networks for patient level insights and biomarker discovery in multi-omics integration","date":"2024-01-31","arxiv_id":"2401.17612","repositories_listed":1,"syntology":null},{"url":"/paper/dgnn-decoupled-graph-neural-networks-with","slug":"dgnn-decoupled-graph-neural-networks-with","title":"DGNN: Decoupled Graph Neural Networks with Structural Consistency between Attribute and Graph Embedding Representations","date":"2024-01-28","arxiv_id":"2401.15584","repositories_listed":1,"syntology":null},{"url":"/paper/improving-expressive-power-of-spectral-graph","slug":"improving-expressive-power-of-spectral-graph","title":"Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction","date":"2024-01-28","arxiv_id":"2401.15603","repositories_listed":1,"syntology":null},{"url":"/paper/cross-space-adaptive-filter-integrating-graph","slug":"cross-space-adaptive-filter-integrating-graph","title":"Cross-Space Adaptive Filter: Integrating Graph Topology and Node Attributes for Alleviating the Over-smoothing Problem","date":"2024-01-26","arxiv_id":"2401.14876","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"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) · 0 unverified","sample_list":"/paper/cross-space-adaptive-filter-integrating-graph#ran","syntology_url":"https://syntology.ai/paper/2401.14876","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.14876"}},"official":{"repos":["huangzichun/cross-space-adaptive-filter"],"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/multitask-active-learning-for-graph-anomaly","slug":"multitask-active-learning-for-graph-anomaly","title":"Multitask Active Learning for Graph Anomaly Detection","date":"2024-01-24","arxiv_id":"2401.13210","repositories_listed":1,"syntology":null},{"url":"/paper/graph-contrastive-invariant-learning-from-the","slug":"graph-contrastive-invariant-learning-from-the","title":"Graph Contrastive Invariant Learning from the Causal Perspective","date":"2024-01-23","arxiv_id":"2401.12564","repositories_listed":1,"syntology":null},{"url":"/paper/mapping-debiasing-graph-neural-networks-for","slug":"mapping-debiasing-graph-neural-networks-for","title":"MAPPING: Debiasing Graph Neural Networks for Fair Node Classification with Limited Sensitive Information Leakage","date":"2024-01-23","arxiv_id":"2401.12824","repositories_listed":1,"syntology":null},{"url":"/paper/disentangled-condensation-for-large-scale","slug":"disentangled-condensation-for-large-scale","title":"Disentangled Condensation for Large-scale Graphs","date":"2024-01-18","arxiv_id":"2401.12231","repositories_listed":1,"syntology":null},{"url":"/paper/infinite-horizon-graph-filters-leveraging","slug":"infinite-horizon-graph-filters-leveraging","title":"Infinite-Horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information Aggregation","date":"2024-01-18","arxiv_id":"2401.09943","repositories_listed":1,"syntology":null},{"url":"/paper/population-graph-cross-network-node","slug":"population-graph-cross-network-node","title":"Population Graph Cross-Network Node Classification for Autism Detection Across Sample Groups","date":"2024-01-10","arxiv_id":"2401.05478","repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-weighted-graph-representation-for","slug":"multimodal-weighted-graph-representation-for","title":"Multimodal weighted graph representation for information extraction from visually rich documents.","date":"2024-01-05","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/strong-transitivity-relations-and-graph","slug":"strong-transitivity-relations-and-graph","title":"Strong Transitivity Relations and Graph Neural Networks","date":"2024-01-01","arxiv_id":"2401.01384","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-aggregations-for-high","slug":"hierarchical-aggregations-for-high","title":"Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding","date":"2023-12-28","arxiv_id":"2312.16834","repositories_listed":1,"syntology":null},{"url":"/paper/mitigating-degree-biases-in-message-passing","slug":"mitigating-degree-biases-in-message-passing","title":"Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures","date":"2023-12-28","arxiv_id":"2312.16788","repositories_listed":1,"syntology":null},{"url":"/paper/graph-coarsening-via-convolution-matching-for","slug":"graph-coarsening-via-convolution-matching-for","title":"Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training","date":"2023-12-24","arxiv_id":"2312.15520","repositories_listed":1,"syntology":null},{"url":"/paper/towards-fine-grained-explainability-for","slug":"towards-fine-grained-explainability-for","title":"Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network","date":"2023-12-23","arxiv_id":"2312.15237","repositories_listed":1,"syntology":null},{"url":"/paper/pc-conv-unifying-homophily-and-heterophily","slug":"pc-conv-unifying-homophily-and-heterophily","title":"PC-Conv: Unifying Homophily and Heterophily with Two-fold Filtering","date":"2023-12-22","arxiv_id":"2312.14438","repositories_listed":1,"syntology":{"n":14,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":14,"phrase":"10 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; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/pc-conv-unifying-homophily-and-heterophily#ran","syntology_url":"https://syntology.ai/paper/2312.14438","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.14438"}},"official":{"repos":["uestclbh/pc-conv"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/puma-efficient-continual-graph-learning-with","slug":"puma-efficient-continual-graph-learning-with","title":"PUMA: Efficient Continual Graph Learning for Node Classification with Graph Condensation","date":"2023-12-22","arxiv_id":"2312.14439","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"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, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/puma-efficient-continual-graph-learning-with#ran","syntology_url":"https://syntology.ai/paper/2312.14439","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.14439"}},"official":{"repos":["superallen13/puma"],"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/dp-adambc-your-dp-adam-is-actually-dp-sgd","slug":"dp-adambc-your-dp-adam-is-actually-dp-sgd","title":"DP-AdamBC: Your DP-Adam Is Actually DP-SGD (Unless You Apply Bias Correction)","date":"2023-12-21","arxiv_id":"2312.14334","repositories_listed":1,"syntology":{"n":15,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":15,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/dp-adambc-your-dp-adam-is-actually-dp-sgd#ran","syntology_url":"https://syntology.ai/paper/2312.14334","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.14334"}},"official":{"repos":["ubc-systopia/DP-AdamBC"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/nodemixup-tackling-under-reaching-for-graph","slug":"nodemixup-tackling-under-reaching-for-graph","title":"NodeMixup: Tackling Under-Reaching for Graph Neural Networks","date":"2023-12-20","arxiv_id":"2312.13032","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/nodemixup-tackling-under-reaching-for-graph#ran","syntology_url":"https://syntology.ai/paper/2312.13032","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.13032"}},"official":{"repos":["weiganglu/nodemixup"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/chasing-fairness-in-graphs-a-gnn-architecture","slug":"chasing-fairness-in-graphs-a-gnn-architecture","title":"Chasing Fairness in Graphs: A GNN Architecture Perspective","date":"2023-12-19","arxiv_id":"2312.12369","repositories_listed":1,"syntology":null},{"url":"/paper/graph-transformers-for-large-graphs","slug":"graph-transformers-for-large-graphs","title":"Graph Transformers for Large Graphs","date":"2023-12-18","arxiv_id":"2312.11109","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":5,"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) · 2 unverified","sample_list":"/paper/graph-transformers-for-large-graphs#ran","syntology_url":"https://syntology.ai/paper/2312.11109","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.11109"}},"official":{"repos":["snap-research/largegt"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/hypergraph-transformer-for-semi-supervised","slug":"hypergraph-transformer-for-semi-supervised","title":"Hypergraph Transformer for Semi-Supervised Classification","date":"2023-12-18","arxiv_id":"2312.11385","repositories_listed":1,"syntology":null},{"url":"/paper/hypergraph-mlp-learning-on-hypergraphs","slug":"hypergraph-mlp-learning-on-hypergraphs","title":"Hypergraph-MLP: Learning on Hypergraphs without Message Passing","date":"2023-12-15","arxiv_id":"2312.09778","repositories_listed":1,"syntology":null},{"url":"/paper/cat-a-causally-graph-attention-network-for","slug":"cat-a-causally-graph-attention-network-for","title":"CAT: A Causally Graph Attention Network for Trimming Heterophilic Graph","date":"2023-12-14","arxiv_id":"2312.08672","repositories_listed":1,"syntology":null},{"url":"/paper/graph-neural-networks-with-diverse-spectral","slug":"graph-neural-networks-with-diverse-spectral","title":"Graph Neural Networks with Diverse Spectral Filtering","date":"2023-12-14","arxiv_id":"2312.09041","repositories_listed":1,"syntology":null},{"url":"/paper/curriculum-enhanced-residual-soft-an","slug":"curriculum-enhanced-residual-soft-an","title":"Curriculum-Enhanced Residual Soft An-Isotropic Normalization for Over-smoothness in Deep GNNs","date":"2023-12-13","arxiv_id":"2312.08221","repositories_listed":1,"syntology":null}],"record_sha256":"391f174ca05c685d903e60fac5254f1109e518a331bf5eab3bb7f74501a60f35","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}