{"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/8","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":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":8,"pages_in_order":20,"rows_per_page":100,"rows":[701,800],"of":1949,"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","prev":"/task/link-prediction/papers/7","next":"/task/link-prediction/papers/9","papers":[{"url":"/paper/multi-task-attentive-residual-networks-for","slug":"multi-task-attentive-residual-networks-for","title":"Multi-Task Attentive Residual Networks for Argument Mining","date":"2021-02-24","arxiv_id":"2102.12227","repositories_listed":1,"syntology":null},{"url":"/paper/pre-training-on-dynamic-graph-neural-networks","slug":"pre-training-on-dynamic-graph-neural-networks","title":"Pre-Training on Dynamic Graph Neural Networks","date":"2021-02-24","arxiv_id":"2102.12380","repositories_listed":1,"syntology":null},{"url":"/paper/v2w-bert-a-framework-for-effective","slug":"v2w-bert-a-framework-for-effective","title":"V2W-BERT: A Framework for Effective Hierarchical Multiclass Classification of Software Vulnerabilities","date":"2021-02-23","arxiv_id":"2102.11498","repositories_listed":1,"syntology":null},{"url":"/paper/magnet-a-magnetic-neural-network-for-directed","slug":"magnet-a-magnetic-neural-network-for-directed","title":"MagNet: A Neural Network for Directed Graphs","date":"2021-02-22","arxiv_id":"2102.11391","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/magnet-a-magnetic-neural-network-for-directed#ran","syntology_url":"https://syntology.ai/paper/2102.11391","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.11391"}},"official":{"repos":["matthew-hirn/magnet"],"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/persistence-homology-for-link-prediction-an","slug":"persistence-homology-for-link-prediction-an","title":"Link Prediction with Persistent Homology: An Interactive View","date":"2021-02-20","arxiv_id":"2102.10255","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/persistence-homology-for-link-prediction-an#ran","syntology_url":"https://syntology.ai/paper/2102.10255","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.10255"}},"official":{"repos":["pkuyzy/TLC-GNN"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/knowledge-hypergraph-embedding-meets","slug":"knowledge-hypergraph-embedding-meets","title":"Knowledge Hypergraph Embedding Meets Relational Algebra","date":"2021-02-18","arxiv_id":"2102.09557","repositories_listed":1,"syntology":null},{"url":"/paper/fast-graph-learning-with-unique-optimal","slug":"fast-graph-learning-with-unique-optimal","title":"Fast Graph Learning with Unique Optimal Solutions","date":"2021-02-17","arxiv_id":"2102.08530","repositories_listed":1,"syntology":null},{"url":"/paper/a-hidden-challenge-of-link-prediction-which","slug":"a-hidden-challenge-of-link-prediction-which","title":"A Hidden Challenge of Link Prediction: Which Pairs to Check?","date":"2021-02-15","arxiv_id":"2102.07878","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-attack-on-network-embeddings-via","slug":"adversarial-attack-on-network-embeddings-via","title":"Adversarial Attack on Network Embeddings via Supervised Network Poisoning","date":"2021-02-14","arxiv_id":"2102.07164","repositories_listed":1,"syntology":null},{"url":"/paper/multiplex-bipartite-network-embedding-using","slug":"multiplex-bipartite-network-embedding-using","title":"Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks","date":"2021-02-12","arxiv_id":"2102.06371","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/multiplex-bipartite-network-embedding-using#ran","syntology_url":"https://syntology.ai/paper/2102.06371","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.06371"}},"official":null}},{"url":"/paper/hyperedge-prediction-using-tensor-eigenvalue","slug":"hyperedge-prediction-using-tensor-eigenvalue","title":"Hyperedge Prediction using Tensor Eigenvalue Decomposition","date":"2021-02-06","arxiv_id":"2102.04986","repositories_listed":1,"syntology":null},{"url":"/paper/re-improving-multi-hop-question-answering","slug":"re-improving-multi-hop-question-answering","title":"[Re] Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings","date":"2021-01-31","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/efficient-graph-deep-learning-in-tensorflow","slug":"efficient-graph-deep-learning-in-tensorflow","title":"Efficient Graph Deep Learning in TensorFlow with tf_geometric","date":"2021-01-27","arxiv_id":"2101.11552","repositories_listed":1,"syntology":null},{"url":"/paper/improving-graph-representation-learning-by","slug":"improving-graph-representation-learning-by","title":"Calibrating and Improving Graph Contrastive Learning","date":"2021-01-27","arxiv_id":"2101.11525","repositories_listed":1,"syntology":null},{"url":"/paper/identity-aware-graph-neural-networks","slug":"identity-aware-graph-neural-networks","title":"Identity-aware Graph Neural Networks","date":"2021-01-25","arxiv_id":"2101.10320","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-generation-variational-bayes-on","slug":"knowledge-generation-variational-bayes-on","title":"Knowledge Generation -- Variational Bayes on Knowledge Graphs","date":"2021-01-21","arxiv_id":"2101.08857","repositories_listed":1,"syntology":null},{"url":"/paper/graphattacker-a-general-multi-task","slug":"graphattacker-a-general-multi-task","title":"GraphAttacker: A General Multi-Task GraphAttack Framework","date":"2021-01-18","arxiv_id":"2101.06855","repositories_listed":1,"syntology":null},{"url":"/paper/membership-inference-attack-on-graph-neural","slug":"membership-inference-attack-on-graph-neural","title":"Membership Inference Attack on Graph Neural Networks","date":"2021-01-17","arxiv_id":"2101.06570","repositories_listed":1,"syntology":null},{"url":"/paper/disentangling-homophily-community-structure","slug":"disentangling-homophily-community-structure","title":"Disentangling homophily, community structure and triadic closure in networks","date":"2021-01-07","arxiv_id":"2101.02510","repositories_listed":1,"syntology":null},{"url":"/paper/graph-edit-networks","slug":"graph-edit-networks","title":"Graph Edit Networks","date":"2021-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/on-dyadic-fairness-exploring-and-mitigating","slug":"on-dyadic-fairness-exploring-and-mitigating","title":"On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections","date":"2021-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-graph-diffusion-networks-with-hop","slug":"adaptive-graph-diffusion-networks-with-hop","title":"Adaptive Graph Diffusion Networks","date":"2020-12-30","arxiv_id":"2012.15024","repositories_listed":1,"syntology":null},{"url":"/paper/relational-boosted-bandits","slug":"relational-boosted-bandits","title":"Relational Boosted Bandits","date":"2020-12-16","arxiv_id":"2012.09220","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-permutation-guided-node","slug":"adversarial-permutation-guided-node","title":"Adversarial Permutation Guided Node Representations for Link Prediction","date":"2020-12-13","arxiv_id":"2012.08974","repositories_listed":1,"syntology":null},{"url":"/paper/bipartite-graph-embedding-via-mutual","slug":"bipartite-graph-embedding-via-mutual","title":"Bipartite Graph Embedding via Mutual Information Maximization","date":"2020-12-10","arxiv_id":"2012.05442","repositories_listed":1,"syntology":null},{"url":"/paper/scalable-and-interpretable-rule-based-link","slug":"scalable-and-interpretable-rule-based-link","title":"Scalable and interpretable rule-based link prediction for large heterogeneous knowledge graphs","date":"2020-12-10","arxiv_id":"2012.05750","repositories_listed":1,"syntology":null},{"url":"/paper/improving-relation-extraction-by-leveraging","slug":"improving-relation-extraction-by-leveraging","title":"Improving Relation Extraction by Leveraging Knowledge Graph Link Prediction","date":"2020-12-09","arxiv_id":"2012.04812","repositories_listed":1,"syntology":null},{"url":"/paper/dr-covid-graph-neural-networks-for-sars-cov-2","slug":"dr-covid-graph-neural-networks-for-sars-cov-2","title":"Dr-COVID: Graph Neural Networks for SARS-CoV-2 Drug Repurposing","date":"2020-12-03","arxiv_id":"2012.02151","repositories_listed":1,"syntology":null},{"url":"/paper/exploiting-node-content-for-multiview-graph","slug":"exploiting-node-content-for-multiview-graph","title":"Exploiting Node Content for Multiview Graph Convolutional Network and Adversarial Regularization","date":"2020-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multi-task-learning-for-knowledge-graph","slug":"multi-task-learning-for-knowledge-graph","title":"Multi-Task Learning for Knowledge Graph Completion with Pre-trained Language Models","date":"2020-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/relation-specific-transformations-for-open","slug":"relation-specific-transformations-for-open","title":"Relation Specific Transformations for Open World Knowledge Graph Completion","date":"2020-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/self-expressive-graph-neural-network-for","slug":"self-expressive-graph-neural-network-for","title":"Unsupervised Constrained Community Detection via Self-Expressive Graph Neural Network","date":"2020-11-28","arxiv_id":"2011.14078","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-signed-link-prediction-with","slug":"interpretable-signed-link-prediction-with","title":"Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph","date":"2020-11-25","arxiv_id":"2011.12517","repositories_listed":1,"syntology":null},{"url":"/paper/revisit-graph-neural-networks-and-distance","slug":"revisit-graph-neural-networks-and-distance","title":"Revisiting graph neural networks and distance encoding from a practical view","date":"2020-11-22","arxiv_id":"2011.12228","repositories_listed":1,"syntology":null},{"url":"/paper/tucker-decomposition-based-temporal-knowledge","slug":"tucker-decomposition-based-temporal-knowledge","title":"Tucker decomposition-based Temporal Knowledge Graph Completion","date":"2020-11-16","arxiv_id":"2011.07751","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-drop-robust-graph-neural-network","slug":"learning-to-drop-robust-graph-neural-network","title":"Learning to Drop: Robust Graph Neural Network via Topological Denoising","date":"2020-11-13","arxiv_id":"2011.07057","repositories_listed":1,"syntology":null},{"url":"/paper/distill2vec-dynamic-graph-representation","slug":"distill2vec-dynamic-graph-representation","title":"Distill2Vec: Dynamic Graph Representation Learning with Knowledge Distillation","date":"2020-11-11","arxiv_id":"2011.05664","repositories_listed":1,"syntology":null},{"url":"/paper/egad-evolving-graph-representation-learning","slug":"egad-evolving-graph-representation-learning","title":"EGAD: Evolving Graph Representation Learning with Self-Attention and Knowledge Distillation for Live Video Streaming Events","date":"2020-11-11","arxiv_id":"2011.05705","repositories_listed":1,"syntology":null},{"url":"/paper/multi-label-classification-using-link","slug":"multi-label-classification-using-link","title":"Multi-Label Classification Using Link Prediction","date":"2020-11-11","arxiv_id":"2011.05476","repositories_listed":1,"syntology":null},{"url":"/paper/vstreamdrls-dynamic-graph-representation","slug":"vstreamdrls-dynamic-graph-representation","title":"VStreamDRLS: Dynamic Graph Representation Learning with Self-Attention for Enterprise Distributed Video Streaming Solutions","date":"2020-11-11","arxiv_id":"2011.05671","repositories_listed":1,"syntology":null},{"url":"/paper/node-attribute-completion-in-knowledge-graphs","slug":"node-attribute-completion-in-knowledge-graphs","title":"Node Attribute Completion in Knowledge Graphs with Multi-Relational Propagation","date":"2020-11-10","arxiv_id":"2011.05301","repositories_listed":1,"syntology":null},{"url":"/paper/two-stage-training-of-graph-neural-networks","slug":"two-stage-training-of-graph-neural-networks","title":"Two-stage Training of Graph Neural Networks for Graph Classification","date":"2020-11-10","arxiv_id":"2011.05097","repositories_listed":1,"syntology":null},{"url":"/paper/erlkg-entity-representation-learning-and","slug":"erlkg-entity-representation-learning-and","title":"ERLKG: Entity Representation Learning and Knowledge Graph based association analysis of COVID-19 through mining of unstructured biomedical corpora","date":"2020-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/hyperbolic-graph-embedding-with-enhanced-semi","slug":"hyperbolic-graph-embedding-with-enhanced-semi","title":"Hyperbolic Graph Embedding with Enhanced Semi-Implicit Variational Inference","date":"2020-10-31","arxiv_id":"2011.00194","repositories_listed":1,"syntology":null},{"url":"/paper/gripnet-graph-information-propagation-on","slug":"gripnet-graph-information-propagation-on","title":"GripNet: Graph Information Propagation on Supergraph for Heterogeneous Graphs","date":"2020-10-29","arxiv_id":"2010.15914","repositories_listed":1,"syntology":null},{"url":"/paper/graph-geometry-interaction-learning","slug":"graph-geometry-interaction-learning","title":"Graph Geometry Interaction Learning","date":"2020-10-23","arxiv_id":"2010.12135","repositories_listed":1,"syntology":null},{"url":"/paper/metapath-and-entity-aware-graph-neural","slug":"metapath-and-entity-aware-graph-neural","title":"Metapath- and Entity-aware Graph Neural Network for Recommendation","date":"2020-10-22","arxiv_id":"2010.11793","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-attentional-network-for-few-shot","slug":"adaptive-attentional-network-for-few-shot","title":"Adaptive Attentional Network for Few-Shot Knowledge Graph Completion","date":"2020-10-19","arxiv_id":"2010.09638","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adaptive-attentional-network-for-few-shot#ran","syntology_url":"https://syntology.ai/paper/2010.09638","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.09638"}},"official":{"repos":["JiaweiSheng/FAAN"],"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/predicting-biomedical-interactions-with","slug":"predicting-biomedical-interactions-with","title":"Predicting Biomedical Interactions with Higher-Order Graph Convolutional Networks","date":"2020-10-16","arxiv_id":"2010.08516","repositories_listed":1,"syntology":null},{"url":"/paper/probabilistic-case-based-reasoning-for-open","slug":"probabilistic-case-based-reasoning-for-open","title":"Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion","date":"2020-10-07","arxiv_id":"2010.03548","repositories_listed":1,"syntology":null},{"url":"/paper/joint-inference-of-structure-and-diffusion-in","slug":"joint-inference-of-structure-and-diffusion-in","title":"Joint Inference of Diffusion and Structure in Partially Observed Social Networks Using Coupled Matrix Factorization","date":"2020-10-03","arxiv_id":"2010.01400","repositories_listed":1,"syntology":null},{"url":"/paper/libkge-a-knowledge-graph-embedding-library","slug":"libkge-a-knowledge-graph-embedding-library","title":"LibKGE - A knowledge graph embedding library for reproducible research","date":"2020-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-graph-normalization-for-graph-neural","slug":"learning-graph-normalization-for-graph-neural","title":"Learning Graph Normalization for Graph Neural Networks","date":"2020-09-24","arxiv_id":"2009.11746","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-graph-normalization-for-graph-neural#ran","syntology_url":"https://syntology.ai/paper/2009.11746","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.11746"}},"official":{"repos":["cyh1112/GraphNormalization"],"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":["unlocated"]}}},{"url":"/paper/message-passing-for-hyper-relational","slug":"message-passing-for-hyper-relational","title":"Message Passing for Hyper-Relational Knowledge Graphs","date":"2020-09-22","arxiv_id":"2009.10847","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":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) · 2 unverified","sample_list":"/paper/message-passing-for-hyper-relational#ran","syntology_url":"https://syntology.ai/paper/2009.10847","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.10847"}},"official":{"repos":["migalkin/StarE"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/em-rbr-a-reinforced-framework-for-knowledge","slug":"em-rbr-a-reinforced-framework-for-knowledge","title":"EM-RBR: a reinforced framework for knowledge graph completion from reasoning perspective","date":"2020-09-18","arxiv_id":"2009.08656","repositories_listed":1,"syntology":null},{"url":"/paper/force2vec-parallel-force-directed-graph","slug":"force2vec-parallel-force-directed-graph","title":"Force2Vec: Parallel force-directed graph embedding","date":"2020-09-17","arxiv_id":"2009.10035","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-coarsening-for-embedding-large","slug":"understanding-coarsening-for-embedding-large","title":"Understanding Coarsening for Embedding Large-Scale Graphs","date":"2020-09-10","arxiv_id":"2009.04925","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-message-passing-graph-neural","slug":"hierarchical-message-passing-graph-neural","title":"Hierarchical Message-Passing Graph Neural Networks","date":"2020-09-08","arxiv_id":"2009.03717","repositories_listed":1,"syntology":null},{"url":"/paper/torchkge-knowledge-graph-embedding-in-python","slug":"torchkge-knowledge-graph-embedding-in-python","title":"TorchKGE: Knowledge Graph Embedding in Python and PyTorch","date":"2020-09-07","arxiv_id":"2009.02963","repositories_listed":1,"syntology":null},{"url":"/paper/a-simple-and-general-graph-neural-network","slug":"a-simple-and-general-graph-neural-network","title":"Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing","date":"2020-09-05","arxiv_id":"2009.02562","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-observed-connections-link-injection","slug":"beyond-observed-connections-link-injection","title":"Beyond Observed Connections : Link Injection","date":"2020-09-02","arxiv_id":"2009.04447","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-privacy-preserving-graph","slug":"adversarial-privacy-preserving-graph","title":"Adversarial Privacy Preserving Graph Embedding against Inference Attack","date":"2020-08-30","arxiv_id":"2008.13072","repositories_listed":1,"syntology":null},{"url":"/paper/item-tagging-for-information-retrieval-a","slug":"item-tagging-for-information-retrieval-a","title":"Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach","date":"2020-08-26","arxiv_id":"2008.11567","repositories_listed":1,"syntology":null},{"url":"/paper/lowfer-low-rank-bilinear-pooling-for-link","slug":"lowfer-low-rank-bilinear-pooling-for-link","title":"LowFER: Low-rank Bilinear Pooling for Link Prediction","date":"2020-08-25","arxiv_id":"2008.10858","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 3 unverified","sample_list":"/paper/lowfer-low-rank-bilinear-pooling-for-link#ran","syntology_url":"https://syntology.ai/paper/2008.10858","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.10858"}},"official":{"repos":["suamin/LowFER"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/multiverse-a-multiplex-and-multiplex","slug":"multiverse-a-multiplex-and-multiplex","title":"MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach","date":"2020-08-23","arxiv_id":"2008.10085","repositories_listed":1,"syntology":null},{"url":"/paper/graphreach-locality-aware-graph-neural","slug":"graphreach-locality-aware-graph-neural","title":"GraphReach: Position-Aware Graph Neural Network using Reachability Estimations","date":"2020-08-19","arxiv_id":"2008.09657","repositories_listed":1,"syntology":null},{"url":"/paper/nase-learning-knowledge-graph-embedding-for","slug":"nase-learning-knowledge-graph-embedding-for","title":"NASE: Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture Search","date":"2020-08-18","arxiv_id":"2008.07723","repositories_listed":1,"syntology":null},{"url":"/paper/tempnodeemb-temporal-node-embedding","slug":"tempnodeemb-temporal-node-embedding","title":"TempNodeEmb:Temporal Node Embedding considering temporal edge influence matrix","date":"2020-08-16","arxiv_id":"2008.06940","repositories_listed":1,"syntology":null},{"url":"/paper/dense-an-enhanced-non-abelian-group","slug":"dense-an-enhanced-non-abelian-group","title":"DensE: An Enhanced Non-commutative Representation for Knowledge Graph Embedding with Adaptive Semantic Hierarchy","date":"2020-08-11","arxiv_id":"2008.04548","repositories_listed":1,"syntology":null},{"url":"/paper/learning-attribute-structure-co-evolutions-in","slug":"learning-attribute-structure-co-evolutions-in","title":"Learning Attribute-Structure Co-Evolutions in Dynamic Graphs","date":"2020-07-25","arxiv_id":"2007.13004","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-of-contextual","slug":"self-supervised-learning-of-contextual","title":"Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks","date":"2020-07-22","arxiv_id":"2007.11192","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-link-prediction-via-graph-neural","slug":"few-shot-link-prediction-via-graph-neural","title":"Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing","date":"2020-07-20","arxiv_id":"2007.10261","repositories_listed":1,"syntology":null},{"url":"/paper/panrep-universal-node-embeddings-for","slug":"panrep-universal-node-embeddings-for","title":"PanRep: Graph neural networks for extracting universal node embeddings in heterogeneous graphs","date":"2020-07-20","arxiv_id":"2007.10445","repositories_listed":1,"syntology":null},{"url":"/paper/second-order-pooling-for-graph-neural","slug":"second-order-pooling-for-graph-neural","title":"Second-Order Pooling for Graph Neural Networks","date":"2020-07-20","arxiv_id":"2007.10467","repositories_listed":1,"syntology":null},{"url":"/paper/the-multilayer-random-dot-product-graph","slug":"the-multilayer-random-dot-product-graph","title":"The multilayer random dot product graph","date":"2020-07-20","arxiv_id":"2007.10455","repositories_listed":1,"syntology":null},{"url":"/paper/inductive-link-prediction-for-nodes-having","slug":"inductive-link-prediction-for-nodes-having","title":"Inductive Link Prediction for Nodes Having Only Attribute Information","date":"2020-07-16","arxiv_id":"2007.08053","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/inductive-link-prediction-for-nodes-having#ran","syntology_url":"https://syntology.ai/paper/2007.08053","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.08053"}},"official":{"repos":["working-yuhao/DEAL"],"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":["official"]}}},{"url":"/paper/self-supervised-auxiliary-learning-with-meta","slug":"self-supervised-auxiliary-learning-with-meta","title":"Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs","date":"2020-07-16","arxiv_id":"2007.08294","repositories_listed":1,"syntology":null},{"url":"/paper/boxe-a-box-embedding-model-for-knowledge-base","slug":"boxe-a-box-embedding-model-for-knowledge-base","title":"BoxE: A Box Embedding Model for Knowledge Base Completion","date":"2020-07-13","arxiv_id":"2007.06267","repositories_listed":1,"syntology":null},{"url":"/paper/generalizing-tensor-decomposition-for-n-ary","slug":"generalizing-tensor-decomposition-for-n-ary","title":"Generalizing Tensor Decomposition for N-ary Relational Knowledge Bases","date":"2020-07-08","arxiv_id":"2007.03988","repositories_listed":1,"syntology":null},{"url":"/paper/faster-graph-embeddings-via-coarsening","slug":"faster-graph-embeddings-via-coarsening","title":"Faster Graph Embeddings via Coarsening","date":"2020-07-06","arxiv_id":"2007.02817","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/faster-graph-embeddings-via-coarsening#ran","syntology_url":"https://syntology.ai/paper/2007.02817","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.02817"}},"official":null}},{"url":"/paper/wiki-cs-a-wikipedia-based-benchmark-for-graph","slug":"wiki-cs-a-wikipedia-based-benchmark-for-graph","title":"Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks","date":"2020-07-06","arxiv_id":"2007.02901","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-graph-encoder-for-attributed-graph","slug":"adaptive-graph-encoder-for-attributed-graph","title":"Adaptive Graph Encoder for Attributed Graph Embedding","date":"2020-07-03","arxiv_id":"2007.01594","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"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: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adaptive-graph-encoder-for-attributed-graph#ran","syntology_url":"https://syntology.ai/paper/2007.01594","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.01594"}},"official":{"repos":["thunlp/AGE"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/can-we-predict-new-facts-with-open-knowledge","slug":"can-we-predict-new-facts-with-open-knowledge","title":"Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/transint-embedding-implication-rules-in-1","slug":"transint-embedding-implication-rules-in-1","title":"TransINT: Embedding Implication Rules in Knowledge Graphs with Isomorphic Intersections of Linear Subspaces","date":"2020-07-01","arxiv_id":"2007.00271","repositories_listed":1,"syntology":null},{"url":"/paper/a-metric-on-directed-graphs-and-markov-chains","slug":"a-metric-on-directed-graphs-and-markov-chains","title":"A metric on directed graphs and Markov chains based on hitting probabilities","date":"2020-06-25","arxiv_id":"2006.14482","repositories_listed":1,"syntology":null},{"url":"/paper/space-time-correspondence-as-a-contrastive","slug":"space-time-correspondence-as-a-contrastive","title":"Space-Time Correspondence as a Contrastive Random Walk","date":"2020-06-25","arxiv_id":"2006.14613","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":3,"phrase":"11 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; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/space-time-correspondence-as-a-contrastive#ran","syntology_url":"https://syntology.ai/paper/2006.14613","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.14613"}},"official":{"repos":["ajabri/videowalk"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-edge-features-for-improved","slug":"self-supervised-edge-features-for-improved","title":"Self-supervised edge features for improved Graph Neural Network training","date":"2020-06-23","arxiv_id":"2007.04777","repositories_listed":1,"syntology":null},{"url":"/paper/a-self-attention-network-based-node-embedding","slug":"a-self-attention-network-based-node-embedding","title":"A Self-Attention Network based Node Embedding Model","date":"2020-06-22","arxiv_id":"2006.12100","repositories_listed":1,"syntology":null},{"url":"/paper/markov-random-geometric-graph-mrgg-a-growth","slug":"markov-random-geometric-graph-mrgg-a-growth","title":"Markov Random Geometric Graph (MRGG): A Growth Model for Temporal Dynamic Networks","date":"2020-06-12","arxiv_id":"2006.07001","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-extrapolate-knowledge","slug":"learning-to-extrapolate-knowledge","title":"Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction","date":"2020-06-11","arxiv_id":"2006.06648","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/learning-to-extrapolate-knowledge#ran","syntology_url":"https://syntology.ai/paper/2006.06648","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.06648"}},"official":{"repos":["JinheonBaek/GEN"],"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/graph-representation-learning-network-via","slug":"graph-representation-learning-network-via","title":"Graph Representation Learning Network via Adaptive Sampling","date":"2020-06-08","arxiv_id":"2006.04637","repositories_listed":1,"syntology":null},{"url":"/paper/persona2vec-a-flexible-multi-role","slug":"persona2vec-a-flexible-multi-role","title":"Persona2vec: A Flexible Multi-role Representations Learning Framework for Graphs","date":"2020-06-04","arxiv_id":"2006.04941","repositories_listed":1,"syntology":null},{"url":"/paper/learning-representations-using-spectral","slug":"learning-representations-using-spectral","title":"Learning Representations using Spectral-Biased Random Walks on Graphs","date":"2020-05-19","arxiv_id":"2005.09752","repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-neural-embeddings-for-link","slug":"benchmarking-neural-embeddings-for-link","title":"Benchmarking neural embeddings for link prediction in knowledge graphs under semantic and structural changes","date":"2020-05-15","arxiv_id":"2005.07654","repositories_listed":1,"syntology":null},{"url":"/paper/asymmetric-transitivity-preserving-graph","slug":"asymmetric-transitivity-preserving-graph","title":"Asymmetric Transitivity Preserving Graph Embedding","date":"2020-05-13","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-base-completion-baseline-strikes","slug":"knowledge-base-completion-baseline-strikes","title":"Knowledge Base Completion: Baseline strikes back (Again)","date":"2020-05-02","arxiv_id":"2005.00804","repositories_listed":1,"syntology":null},{"url":"/paper/seek-segmented-embedding-of-knowledge-graphs","slug":"seek-segmented-embedding-of-knowledge-graphs","title":"SEEK: Segmented Embedding of Knowledge Graphs","date":"2020-05-02","arxiv_id":"2005.00856","repositories_listed":1,"syntology":null},{"url":"/paper/temporal-knowledge-base-completion-new","slug":"temporal-knowledge-base-completion-new","title":"Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols","date":"2020-05-02","arxiv_id":"2005.05035","repositories_listed":1,"syntology":{"n":12,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":12,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/temporal-knowledge-base-completion-new#ran","syntology_url":"https://syntology.ai/paper/2005.05035","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.05035"}},"official":{"repos":["dair-iitd/tkbi"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/a-joint-framework-for-inductive","slug":"a-joint-framework-for-inductive","title":"Explainable Link Prediction for Emerging Entities in Knowledge Graphs","date":"2020-05-01","arxiv_id":"2005.00637","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-base-inference-for-regular","slug":"knowledge-base-inference-for-regular","title":"Regex Queries over Incomplete Knowledge Bases","date":"2020-05-01","arxiv_id":"2005.00480","repositories_listed":1,"syntology":null}],"record_sha256":"568d6c8bab7a050926c543b358fb7d255c810955534fcc4bd0465217ee8162c1","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}