{"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/link-prediction/papers/ran/3","list_of":"/task/link-prediction","task":"Link Prediction","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":3,"pages_in_order":3,"rows_per_page":100,"rows":[201,233],"of":233,"counts":{"archive_papers_tagged":1949,"with_a_code_link":974,"where_syntology_ran_a_sample":233,"not_listed_spam_title":0,"listed":1949,"listed_where_code_ran":233,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":206,"every_run_a_failure_of_syntologys_instrument":27,"listed_with_a_run_with_no_instrument_failure":206,"listed_every_run_a_failure_of_syntologys_instrument":27,"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/link-prediction/papers/ran/1","prev":"/task/link-prediction/papers/ran/2","next":null,"papers":[{"url":"/paper/on-the-use-of-arxiv-as-a-dataset","slug":"on-the-use-of-arxiv-as-a-dataset","title":"On the Use of ArXiv as a Dataset","date":"2019-04-30","arxiv_id":"1905.00075","repositories_listed":1,"syntology":{"n":19,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/on-the-use-of-arxiv-as-a-dataset#ran","syntology_url":"https://syntology.ai/paper/1905.00075","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.00075"}},"official":{"repos":["mattbierbaum/arxiv-public-datasets"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/linkage-based-face-clustering-via-graph","slug":"linkage-based-face-clustering-via-graph","title":"Linkage Based Face Clustering via Graph Convolution Network","date":"2019-03-27","arxiv_id":"1903.11306","repositories_listed":4,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"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) · 3 unverified","sample_list":"/paper/linkage-based-face-clustering-via-graph#ran","syntology_url":"https://syntology.ai/paper/1903.11306","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.11306"}},"official":{"repos":["Zhongdao/gcn_clustering"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/190412575","slug":"190412575","title":"Knowledge Graph Convolutional Networks for Recommender Systems","date":"2019-03-18","arxiv_id":"1904.12575","repositories_listed":8,"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":1,"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/190412575#ran","syntology_url":"https://syntology.ai/paper/1904.12575","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.12575"}},"official":{"repos":["hwwang55/KGCN"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/gnn-explainer-a-tool-for-post-hoc-explanation","slug":"gnn-explainer-a-tool-for-post-hoc-explanation","title":"GNNExplainer: Generating Explanations for Graph Neural Networks","date":"2019-03-10","arxiv_id":"1903.03894","repositories_listed":12,"syntology":{"n":22,"n_ran":18,"n_constructed":0,"n_ran_checked":18,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":18,"n_pointer_only":1,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 18 with no instrument failure: 0 honoured, 0 violated, 18 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/gnn-explainer-a-tool-for-post-hoc-explanation#ran","syntology_url":"https://syntology.ai/paper/1903.03894","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.03894"}},"official":{"repos":["RexYing/gnn-model-explainer"],"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":["listed","official"]}}},{"url":"/paper/graphvite-a-high-performance-cpu-gpu-hybrid","slug":"graphvite-a-high-performance-cpu-gpu-hybrid","title":"GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding","date":"2019-03-02","arxiv_id":"1903.00757","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/graphvite-a-high-performance-cpu-gpu-hybrid#ran","syntology_url":"https://syntology.ai/paper/1903.00757","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.00757"}},"official":null}},{"url":"/paper/evolvegcn-evolving-graph-convolutional","slug":"evolvegcn-evolving-graph-convolutional","title":"EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs","date":"2019-02-26","arxiv_id":"1902.10191","repositories_listed":10,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":1,"phrase":"9 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/evolvegcn-evolving-graph-convolutional#ran","syntology_url":"https://syntology.ai/paper/1902.10191","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.10191"}},"official":{"repos":["IBM/AMLSim","IBM/EvolveGCN"],"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":["listed","official"]}}},{"url":"/paper/rotate-knowledge-graph-embedding-by","slug":"rotate-knowledge-graph-embedding-by","title":"RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space","date":"2019-02-26","arxiv_id":"1902.10197","repositories_listed":10,"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/rotate-knowledge-graph-embedding-by#ran","syntology_url":"https://syntology.ai/paper/1902.10197","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.10197"}},"official":{"repos":["DeepGraphLearning/KnowledgeGraphEmbedding"],"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/gcn-gan-a-non-linear-temporal-link-prediction","slug":"gcn-gan-a-non-linear-temporal-link-prediction","title":"GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks","date":"2019-01-26","arxiv_id":"1901.09165","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":1,"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/gcn-gan-a-non-linear-temporal-link-prediction#ran","syntology_url":"https://syntology.ai/paper/1901.09165","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.09165"}},"official":null}},{"url":"/paper/adversarial-autoencoders-with-constant","slug":"adversarial-autoencoders-with-constant","title":"Adversarial Autoencoders with Constant-Curvature Latent Manifolds","date":"2018-12-11","arxiv_id":"1812.04314","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/adversarial-autoencoders-with-constant#ran","syntology_url":"https://syntology.ai/paper/1812.04314","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.04314"}},"official":{"repos":["danielegrattarola/ccm-aae"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-deep-sequential-model-for-discourse-parsing","slug":"a-deep-sequential-model-for-discourse-parsing","title":"A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues","date":"2018-12-01","arxiv_id":"1812.00176","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/a-deep-sequential-model-for-discourse-parsing#ran","syntology_url":"https://syntology.ai/paper/1812.00176","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.00176"}},"official":{"repos":["shizhouxing/DialogueDiscourseParsing"],"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/differentiating-concepts-and-instances-for","slug":"differentiating-concepts-and-instances-for","title":"Differentiating Concepts and Instances for Knowledge Graph Embedding","date":"2018-11-12","arxiv_id":"1811.04588","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/differentiating-concepts-and-instances-for#ran","syntology_url":"https://syntology.ai/paper/1811.04588","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.04588"}},"official":{"repos":["davidlvxin/TransC"],"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/multi-task-graph-autoencoders","slug":"multi-task-graph-autoencoders","title":"Multi-Task Graph Autoencoders","date":"2018-11-07","arxiv_id":"1811.02798","repositories_listed":1,"syntology":{"n":9,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":6,"n_honours":1,"n_violates":1,"n_no_contract":1,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 1 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/multi-task-graph-autoencoders#ran","syntology_url":"https://syntology.ai/paper/1811.02798","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.02798"}},"official":{"repos":["vuptran/graph-representation-learning"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/a-capsule-network-based-embedding-model-for-1","slug":"a-capsule-network-based-embedding-model-for-1","title":"A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization","date":"2018-08-13","arxiv_id":"1808.04122","repositories_listed":2,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/a-capsule-network-based-embedding-model-for-1#ran","syntology_url":"https://syntology.ai/paper/1808.04122","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.04122"}},"official":{"repos":["daiquocnguyen/CapsE"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/hierarchical-graph-representation-learning","slug":"hierarchical-graph-representation-learning","title":"Hierarchical Graph Representation Learning with Differentiable Pooling","date":"2018-06-22","arxiv_id":"1806.08804","repositories_listed":14,"syntology":{"n":20,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":10,"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) · 10 unverified","sample_list":"/paper/hierarchical-graph-representation-learning#ran","syntology_url":"https://syntology.ai/paper/1806.08804","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.08804"}},"official":null}},{"url":"/paper/canonical-tensor-decomposition-for-knowledge","slug":"canonical-tensor-decomposition-for-knowledge","title":"Canonical Tensor Decomposition for Knowledge Base Completion","date":"2018-06-19","arxiv_id":"1806.07297","repositories_listed":3,"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/canonical-tensor-decomposition-for-knowledge#ran","syntology_url":"https://syntology.ai/paper/1806.07297","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.07297"}},"official":{"repos":["facebookresearch/kbc"],"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":["listed","official"]}}},{"url":"/paper/hyperspherical-variational-auto-encoders","slug":"hyperspherical-variational-auto-encoders","title":"Hyperspherical Variational Auto-Encoders","date":"2018-04-03","arxiv_id":"1804.00891","repositories_listed":9,"syntology":{"n":14,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":6,"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) · 6 unverified","sample_list":"/paper/hyperspherical-variational-auto-encoders#ran","syntology_url":"https://syntology.ai/paper/1804.00891","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.00891"}},"official":{"repos":["nicola-decao/s-vae","nicola-decao/s-vae-pytorch","nicola-decao/s-vae-tf"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed"]}}},{"url":"/paper/locally-private-bayesian-inference-for-count","slug":"locally-private-bayesian-inference-for-count","title":"Locally Private Bayesian Inference for Count Models","date":"2018-03-22","arxiv_id":"1803.08471","repositories_listed":1,"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/locally-private-bayesian-inference-for-count#ran","syntology_url":"https://syntology.ai/paper/1803.08471","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.08471"}},"official":null}},{"url":"/paper/verse-versatile-graph-embeddings-from","slug":"verse-versatile-graph-embeddings-from","title":"VERSE: Versatile Graph Embeddings from Similarity Measures","date":"2018-03-13","arxiv_id":"1803.04742","repositories_listed":2,"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/verse-versatile-graph-embeddings-from#ran","syntology_url":"https://syntology.ai/paper/1803.04742","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.04742"}},"official":{"repos":["xgfs/verse"],"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/netgan-generating-graphs-via-random-walks","slug":"netgan-generating-graphs-via-random-walks","title":"NetGAN: Generating Graphs via Random Walks","date":"2018-03-02","arxiv_id":"1803.00816","repositories_listed":2,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/netgan-generating-graphs-via-random-walks#ran","syntology_url":"https://syntology.ai/paper/1803.00816","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.00816"}},"official":null}},{"url":"/paper/a-novel-embedding-model-for-knowledge-base","slug":"a-novel-embedding-model-for-knowledge-base","title":"A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network","date":"2017-12-06","arxiv_id":"1712.02121","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/a-novel-embedding-model-for-knowledge-base#ran","syntology_url":"https://syntology.ai/paper/1712.02121","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1712.02121"}},"official":{"repos":["daiquocnguyen/ConvKB"],"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/kbgan-adversarial-learning-for-knowledge","slug":"kbgan-adversarial-learning-for-knowledge","title":"KBGAN: Adversarial Learning for Knowledge Graph Embeddings","date":"2017-11-11","arxiv_id":"1711.04071","repositories_listed":3,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/kbgan-adversarial-learning-for-knowledge#ran","syntology_url":"https://syntology.ai/paper/1711.04071","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.04071"}},"official":{"repos":["cai-lw/KBGAN"],"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":["listed","official"]}}},{"url":"/paper/open-world-knowledge-graph-completion","slug":"open-world-knowledge-graph-completion","title":"Open-World Knowledge Graph Completion","date":"2017-11-09","arxiv_id":"1711.03438","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/open-world-knowledge-graph-completion#ran","syntology_url":"https://syntology.ai/paper/1711.03438","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.03438"}},"official":{"repos":["bxshi/ConMask"],"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/graph-attention-networks","slug":"graph-attention-networks","title":"Graph Attention Networks","date":"2017-10-30","arxiv_id":"1710.10903","repositories_listed":93,"syntology":{"n":106,"n_ran":61,"n_constructed":28,"n_ran_checked":52,"n_instrument":9,"n_unverified":45,"n_honours":1,"n_violates":2,"n_no_contract":49,"n_pointer_only":46,"phrase":"61 ran (of which 28 constructed an object rather than computing a result; 52 with no instrument failure: 1 honoured, 2 violated, 49 with no contract checked; 9 where Syntology's instrument failed) · 45 unverified","sample_list":"/paper/graph-attention-networks#ran","syntology_url":"https://syntology.ai/paper/1710.10903","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.10903"}},"official":{"repos":["PetarV-/GAT"],"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/attention-is-all-you-need","slug":"attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","arxiv_id":"1706.03762","repositories_listed":595,"syntology":{"n":946,"n_ran":610,"n_constructed":293,"n_ran_checked":529,"n_instrument":81,"n_unverified":336,"n_honours":45,"n_violates":15,"n_no_contract":469,"n_pointer_only":451,"phrase":"610 ran (of which 293 constructed an object rather than computing a result; 529 with no instrument failure: 45 honoured, 15 violated, 469 with no contract checked; 81 where Syntology's instrument failed) · 336 unverified","sample_list":"/paper/attention-is-all-you-need#ran","syntology_url":"https://syntology.ai/paper/1706.03762","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1706.03762"}},"official":{"repos":["tensorflow/tensor2tensor"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"url":"/paper/inductive-representation-learning-on-large","slug":"inductive-representation-learning-on-large","title":"Inductive Representation Learning on Large Graphs","date":"2017-06-07","arxiv_id":"1706.02216","repositories_listed":20,"syntology":{"n":5,"n_ran":3,"n_constructed":2,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"3 ran (of which 2 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/inductive-representation-learning-on-large#ran","syntology_url":"https://syntology.ai/paper/1706.02216","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1706.02216"}},"official":{"repos":["williamleif/GraphSAGE"],"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/modeling-relational-data-with-graph","slug":"modeling-relational-data-with-graph","title":"Modeling Relational Data with Graph Convolutional Networks","date":"2017-03-17","arxiv_id":"1703.06103","repositories_listed":27,"syntology":{"n":32,"n_ran":18,"n_constructed":3,"n_ran_checked":13,"n_instrument":5,"n_unverified":14,"n_honours":1,"n_violates":1,"n_no_contract":11,"n_pointer_only":15,"phrase":"18 ran (of which 3 constructed an object rather than computing a result; 13 with no instrument failure: 1 honoured, 1 violated, 11 with no contract checked; 5 where Syntology's instrument failed) · 14 unverified","sample_list":"/paper/modeling-relational-data-with-graph#ran","syntology_url":"https://syntology.ai/paper/1703.06103","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.06103"}},"official":{"repos":["tkipf/relational-gcn"],"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":["listed","official","unlocated"]}}},{"url":"/paper/variational-graph-auto-encoders","slug":"variational-graph-auto-encoders","title":"Variational Graph Auto-Encoders","date":"2016-11-21","arxiv_id":"1611.07308","repositories_listed":22,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":7,"n_instrument":4,"n_unverified":2,"n_honours":3,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 3 honoured, 0 violated, 4 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/variational-graph-auto-encoders#ran","syntology_url":"https://syntology.ai/paper/1611.07308","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1611.07308"}},"official":{"repos":["tkipf/gae"],"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/natural-parameter-networks-a-class-of","slug":"natural-parameter-networks-a-class-of","title":"Natural-Parameter Networks: A Class of Probabilistic Neural Networks","date":"2016-11-02","arxiv_id":"1611.00448","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":3,"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; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/natural-parameter-networks-a-class-of#ran","syntology_url":"https://syntology.ai/paper/1611.00448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1611.00448"}},"official":{"repos":["js05212/PyTorch-for-NPN"],"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/higher-order-factorization-machines","slug":"higher-order-factorization-machines","title":"Higher-Order Factorization Machines","date":"2016-07-25","arxiv_id":"1607.07195","repositories_listed":4,"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/higher-order-factorization-machines#ran","syntology_url":"https://syntology.ai/paper/1607.07195","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1607.07195"}},"official":null}},{"url":"/paper/node2vec-scalable-feature-learning-for","slug":"node2vec-scalable-feature-learning-for","title":"node2vec: Scalable Feature Learning for Networks","date":"2016-07-03","arxiv_id":"1607.00653","repositories_listed":20,"syntology":{"n":25,"n_ran":15,"n_constructed":2,"n_ran_checked":13,"n_instrument":2,"n_unverified":10,"n_honours":2,"n_violates":0,"n_no_contract":11,"n_pointer_only":3,"phrase":"15 ran (of which 2 constructed an object rather than computing a result; 13 with no instrument failure: 2 honoured, 0 violated, 11 with no contract checked; 2 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/node2vec-scalable-feature-learning-for#ran","syntology_url":"https://syntology.ai/paper/1607.00653","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1607.00653"}},"official":{"repos":["aditya-grover/node2vec"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"url":"/paper/holographic-embeddings-of-knowledge-graphs","slug":"holographic-embeddings-of-knowledge-graphs","title":"Holographic Embeddings of Knowledge Graphs","date":"2015-10-16","arxiv_id":"1510.04935","repositories_listed":4,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/holographic-embeddings-of-knowledge-graphs#ran","syntology_url":"https://syntology.ai/paper/1510.04935","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1510.04935"}},"official":null}},{"url":"/paper/line-large-scale-information-network","slug":"line-large-scale-information-network","title":"LINE: Large-scale Information Network Embedding","date":"2015-03-12","arxiv_id":"1503.03578","repositories_listed":9,"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/line-large-scale-information-network#ran","syntology_url":"https://syntology.ai/paper/1503.03578","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1503.03578"}},"official":{"repos":["tangjianpku/LINE"],"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/estimation-of-simultaneously-sparse-and-low","slug":"estimation-of-simultaneously-sparse-and-low","title":"Estimation of Simultaneously Sparse and Low Rank Matrices","date":"2012-06-27","arxiv_id":"1206.6474","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":3,"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/estimation-of-simultaneously-sparse-and-low#ran","syntology_url":"https://syntology.ai/paper/1206.6474","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1206.6474"}},"official":null}}],"record_sha256":"ccfe3d587faf42b24d968539dba562f3c61b0cb62524886cca4b6ef0e1a49940","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}