{"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/16","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":16,"pages_in_order":20,"rows_per_page":100,"rows":[1501,1600],"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/15","next":"/task/link-prediction/papers/17","papers":[{"url":null,"slug":"hyperbolic-temporal-knowledge-graph","title":"Hyperbolic Temporal Knowledge Graph Embeddings with Relational and Time Curvatures","date":"2021-06-08","arxiv_id":"2106.04311","repositories_listed":0,"syntology":null},{"url":null,"slug":"network-estimation-by-mixing-adaptivity-and","title":"Network Estimation by Mixing: Adaptivity and More","date":"2021-06-05","arxiv_id":"2106.02803","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-network-learning-with-partially-aligned","title":"Cross-Network Learning with Partially Aligned Graph Convolutional Networks","date":"2021-06-03","arxiv_id":"2106.01583","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-representation-over-dynamic-graph","title":"Learning Representation over Dynamic Graph using Aggregation-Diffusion Mechanism","date":"2021-06-03","arxiv_id":"2106.01678","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-hierarchical-relation-extraction","title":"End-to-End Hierarchical Relation Extraction for Generic Form Understanding","date":"2021-06-02","arxiv_id":"2106.00980","repositories_listed":0,"syntology":null},{"url":null,"slug":"motif-prediction-with-graph-neural-networks","title":"Motif Prediction with Graph Neural Networks","date":"2021-05-26","arxiv_id":"2106.00761","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-based-link-prediction-between-human","title":"Graph Based Link Prediction between Human Phenotypes and Genes","date":"2021-05-25","arxiv_id":"2105.11989","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-links-on-wikipedia-with-anchor","title":"Predicting Links on Wikipedia with Anchor Text Information","date":"2021-05-25","arxiv_id":"2105.11734","repositories_listed":0,"syntology":null},{"url":null,"slug":"auxiliary-learning-induced-graph","title":"Auxiliary learning induced graph convolutional networks","date":"2021-05-21","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-preference-random-walk-algorithm-for-link","title":"A Preference Random Walk Algorithm for Link Prediction through Mutual Influence Nodes in Complex Networks","date":"2021-05-20","arxiv_id":"2105.09494","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-and-improvement-of-adversarial","title":"Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective","date":"2021-05-17","arxiv_id":"2105.08007","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-symbolic-commonsense-reasoner-with","title":"Neural-Symbolic Commonsense Reasoner with Relation Predictors","date":"2021-05-14","arxiv_id":"2105.06717","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-learning-a-survey","title":"Graph Learning: A Survey","date":"2021-05-03","arxiv_id":"2105.00696","repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-learning-using-graph","title":"ResVGAE: Going Deeper with Residual Modules for Link Prediction","date":"2021-05-03","arxiv_id":"2105.00695","repositories_listed":0,"syntology":null},{"url":null,"slug":"muse-multi-faceted-attention-for-signed","title":"MUSE: Multi-faceted Attention for Signed Network Embedding","date":"2021-04-29","arxiv_id":"2104.14449","repositories_listed":0,"syntology":null},{"url":null,"slug":"network-embedding-via-deep-prediction-model","title":"Network Embedding via Deep Prediction Model","date":"2021-04-27","arxiv_id":"2104.13323","repositories_listed":0,"syntology":null},{"url":null,"slug":"seastar-vertex-centric-programming-for-graph","title":"Seastar: vertex-centric programming for graph neural networks","date":"2021-04-21","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hyper-2-hyperbolic-poincare-embedding-for","title":"HYPER^2: Hyperbolic Poincare Embedding for Hyper-Relational Link Prediction","date":"2021-04-20","arxiv_id":"2104.09871","repositories_listed":0,"syntology":null},{"url":null,"slug":"locate-who-you-are-matching-geo-location-to","title":"Locate Who You Are: Matching Geo-location to Text for User Identity Linkage","date":"2021-04-19","arxiv_id":"2104.09119","repositories_listed":0,"syntology":null},{"url":null,"slug":"cear-cross-entity-aware-reranker-for","title":"CEAR: Cross-Entity Aware Reranker for Knowledge Base Completion","date":"2021-04-18","arxiv_id":"2104.08741","repositories_listed":0,"syntology":null},{"url":null,"slug":"membership-inference-attacks-on-knowledge","title":"Membership Inference Attacks on Knowledge Graphs","date":"2021-04-16","arxiv_id":"2104.08273","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hyperbolic-to-hyperbolic-graph","title":"A Hyperbolic-to-Hyperbolic Graph Convolutional Network","date":"2021-04-14","arxiv_id":"2104.06942","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-attributed-network-representation","title":"Deep Attributed Network Representation Learning via Attribute Enhanced Neighborhood","date":"2021-04-12","arxiv_id":"2104.05234","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-representation-learning-for-scientific","title":"On Representation Learning for Scientific News Articles Using Heterogeneous Knowledge Graphs","date":"2021-04-12","arxiv_id":"2104.05866","repositories_listed":0,"syntology":null},{"url":null,"slug":"personalized-bundle-recommendation-in-online","title":"Personalized Bundle Recommendation in Online Games","date":"2021-04-12","arxiv_id":"2104.05307","repositories_listed":0,"syntology":null},{"url":null,"slug":"edge-enriching-knowledge-graph-embeddings","title":"Edge: Enriching Knowledge Graph Embeddings with External Text","date":"2021-04-11","arxiv_id":"2104.04909","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-in-practice-lessons-from-a","title":"Domain adaptation in practice: Lessons from a real-world information extraction pipeline","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-graph-node-correlations-with","title":"Modeling Graph Node Correlations with Neighbor Mixture Models","date":"2021-03-29","arxiv_id":"2103.15966","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-efficiency-euclidean-based-models-for","title":"Hyperbolic Geometry is Not Necessary: Lightweight Euclidean-Based Models for Low-Dimensional Knowledge Graph Embeddings","date":"2021-03-27","arxiv_id":"2103.14930","repositories_listed":0,"syntology":null},{"url":null,"slug":"social-link-inference-via-multi-view-matching","title":"Social Link Inference via Multi-View Matching Network from Spatio-Temporal Trajectories","date":"2021-03-20","arxiv_id":"2103.11095","repositories_listed":0,"syntology":null},{"url":null,"slug":"gcn-alp-addressing-matching-collisions-in","title":"GCN-ALP: Addressing Matching Collisions in Anchor Link Prediction","date":"2021-03-19","arxiv_id":"2103.10600","repositories_listed":0,"syntology":null},{"url":null,"slug":"chronor-rotation-based-temporal-knowledge","title":"ChronoR: Rotation Based Temporal Knowledge Graph Embedding","date":"2021-03-18","arxiv_id":"2103.10379","repositories_listed":0,"syntology":null},{"url":null,"slug":"collaborative-filtering-approach-to-link","title":"Collaborative Filtering Approach to Link Prediction","date":"2021-03-14","arxiv_id":"2103.09907","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynacpd-embedding-algorithm-for-prediction","title":"DynACPD Embedding Algorithm for Prediction Tasks in Dynamic Networks","date":"2021-03-12","arxiv_id":"2103.07080","repositories_listed":0,"syntology":null},{"url":null,"slug":"metapaths-guided-neighbors-aggregated-network","title":"Metapaths guided Neighbors aggregated Network for?Heterogeneous Graph Reasoning","date":"2021-03-11","arxiv_id":"2103.06474","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-relational-learning-perspective-to-multi","title":"A Relational-learning Perspective to Multi-label Chest X-ray Classification","date":"2021-03-10","arxiv_id":"2103.06220","repositories_listed":0,"syntology":null},{"url":null,"slug":"netvec-a-scalable-hypergraph-embedding-system","title":"Scalable Hypergraph Embedding System","date":"2021-03-09","arxiv_id":"2103.09660","repositories_listed":0,"syntology":null},{"url":null,"slug":"universal-representation-for-code","title":"Universal Representation for Code","date":"2021-03-04","arxiv_id":"2103.03116","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmarking-graph-neural-networks-on-link","title":"Benchmarking Graph Neural Networks on Link Prediction","date":"2021-02-24","arxiv_id":"2102.12557","repositories_listed":0,"syntology":null},{"url":null,"slug":"graphfool-targeted-label-adversarial-attack","title":"Graphfool: Targeted Label Adversarial Attack on Graph Embedding","date":"2021-02-24","arxiv_id":"2102.12284","repositories_listed":0,"syntology":null},{"url":null,"slug":"progresses-and-challenges-in-link-prediction","title":"Progresses and Challenges in Link Prediction","date":"2021-02-23","arxiv_id":"2102.11472","repositories_listed":0,"syntology":null},{"url":null,"slug":"link-prediction-approach-to-recommender","title":"Link Prediction Approach to Recommender Systems","date":"2021-02-18","arxiv_id":"2102.09185","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-amount-snapshot-multigraph-for","title":"Temporal-Amount Snapshot MultiGraph for Ethereum Transaction Tracking","date":"2021-02-16","arxiv_id":"2102.08013","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-graph-embedding-using-graph","title":"Knowledge Graph Embedding using Graph Convolutional Networks with Relation-Aware Attention","date":"2021-02-14","arxiv_id":"2102.07200","repositories_listed":0,"syntology":null},{"url":null,"slug":"bridging-graph-neural-networks-and","title":"A Statistical Relational Approach to Learning Distance-based GCNs","date":"2021-02-13","arxiv_id":"2102.07007","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-higher-order-structures-in","title":"Understanding Higher-order Structures in Evolving Graphs: A Simplicial Complex based Kernel Estimation Approach","date":"2021-02-06","arxiv_id":"2102.03609","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-the-limits-of-few-shot-link","title":"Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs","date":"2021-02-05","arxiv_id":"2102.03419","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-graph-representations","title":"Learning Graph Representations","date":"2021-02-03","arxiv_id":"2102.02026","repositories_listed":0,"syntology":null},{"url":null,"slug":"linklouvain-link-aware-a-b-testing-and-its","title":"LinkLouvain: Link-Aware A/B Testing and Its Application on Online Marketing Campaign","date":"2021-02-03","arxiv_id":"2102.01902","repositories_listed":0,"syntology":null},{"url":null,"slug":"heterogeneous-graph-based-deep-learning-for","title":"Heterogeneous Graph based Deep Learning for Biomedical Network Link Prediction","date":"2021-01-28","arxiv_id":"2102.01649","repositories_listed":0,"syntology":null},{"url":null,"slug":"nemr-network-embedding-on-metric-of-relation","title":"NEMR: Network Embedding on Metric of Relation","date":"2021-01-20","arxiv_id":"2101.08020","repositories_listed":0,"syntology":null},{"url":null,"slug":"jitune-just-in-time-hyperparameter-tuning-for","title":"JITuNE: Just-In-Time Hyperparameter Tuning for Network Embedding Algorithms","date":"2021-01-16","arxiv_id":"2101.06427","repositories_listed":0,"syntology":null},{"url":null,"slug":"bigcn-a-bi-directional-low-pass-filtering-1","title":"BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network","date":"2021-01-14","arxiv_id":"2101.05519","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-embedding-dynamic-graphs","title":"A Survey on Embedding Dynamic Graphs","date":"2021-01-04","arxiv_id":"2101.01229","repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-learning-of-reconstructed","title":"Representation Learning of Reconstructed Graphs Using Random Walk Graph Convolutional Network","date":"2021-01-02","arxiv_id":"2101.00417","repositories_listed":0,"syntology":null},{"url":null,"slug":"coldexpand-semi-supervised-graph-learning-in","title":"ColdExpand: Semi-Supervised Graph Learning in Cold Start","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-subgraph-reasoning-for","title":"Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-structural-aggregation-for-explainable","title":"Graph Structural Aggregation for Explainable Learning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"higher-order-structure-prediction-in-evolving","title":"Higher-order Structure Prediction in Evolving Graph Simplicial Complexes","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"incremental-learning-on-growing-graphs","title":"Incremental Learning on Growing Graphs","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"polar-embedding","title":"Polar Embedding","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-relation-learning-with-semantic","title":"Generalized Relation Learning with Semantic Correlation Awareness for Link Prediction","date":"2020-12-22","arxiv_id":"2012.11957","repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-the-performance-of-graph-neural","title":"Analyzing the Performance of Graph Neural Networks with Pipe Parallelism","date":"2020-12-20","arxiv_id":"2012.10840","repositories_listed":0,"syntology":null},{"url":null,"slug":"biomedical-knowledge-graph-refinement-and","title":"Biomedical Knowledge Graph Refinement and Completion using Graph Representation Learning and Top-K Similarity Measure","date":"2020-12-18","arxiv_id":"2012.10540","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-graph-embedding-methods-and","title":"Understanding graph embedding methods and their applications","date":"2020-12-15","arxiv_id":"2012.08019","repositories_listed":0,"syntology":null},{"url":null,"slug":"pair-view-unsupervised-graph-representation","title":"Pair-view Unsupervised Graph Representation Learning","date":"2020-12-11","arxiv_id":"2012.06113","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-link-prediction-for-privacy","title":"Explainable Link Prediction for Privacy-Preserving Contact Tracing","date":"2020-12-10","arxiv_id":"2012.05516","repositories_listed":0,"syntology":null},{"url":null,"slug":"ppke-knowledge-representation-learning-by","title":"PPKE: Knowledge Representation Learning by Path-based Pre-training","date":"2020-12-07","arxiv_id":"2012.03573","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-adversarially-robust","title":"Unsupervised Adversarially-Robust Representation Learning on Graphs","date":"2020-12-04","arxiv_id":"2012.02486","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-based-aspect-representation-learning","title":"Graph-based Aspect Representation Learning for Entity Resolution","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-interact-an-adaptive-interaction","title":"Learning to Interact: An Adaptive Interaction Framework for Knowledge Graph Embeddings","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-heterogeneous-graph-embedding","title":"A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources","date":"2020-11-30","arxiv_id":"2011.14867","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditional-link-prediction-of-category","title":"Conditional Link Prediction of Category-Implicit Keypoint Detection","date":"2020-11-29","arxiv_id":"2011.14462","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-embeddings-via-matrix-factorization-for","title":"Neural graph embeddings as explicit low-rank matrix factorization for link prediction","date":"2020-11-16","arxiv_id":"2011.09907","repositories_listed":0,"syntology":null},{"url":null,"slug":"link-prediction-in-multiplex-networks-via","title":"Link prediction in multiplex networks via triadic closure","date":"2020-11-16","arxiv_id":"2011.09126","repositories_listed":0,"syntology":null},{"url":null,"slug":"association-rules-enhanced-knowledge-graph","title":"Association Rules Enhanced Knowledge Graph Attention Network","date":"2020-11-14","arxiv_id":"2011.08431","repositories_listed":0,"syntology":null},{"url":null,"slug":"theoretical-knowledge-graph-reasoning-via","title":"Theoretical Rule-based Knowledge Graph Reasoning by Connectivity Dependency Discovery","date":"2020-11-12","arxiv_id":"2011.06174","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-edge-centric-network-embeddings","title":"Toward Edge-Centric Network Embeddings","date":"2020-11-11","arxiv_id":"2011.05650","repositories_listed":0,"syntology":null},{"url":null,"slug":"relation-weighted-link-prediction-for-disease","title":"Relation-weighted Link Prediction for Disease Gene Identification","date":"2020-11-10","arxiv_id":"2011.05138","repositories_listed":0,"syntology":null},{"url":null,"slug":"linking-openstreetmap-with-knowledge-graphs","title":"Linking OpenStreetMap with Knowledge Graphs -- Link Discovery for Schema-Agnostic Volunteered Geographic Information","date":"2020-11-06","arxiv_id":"2011.05841","repositories_listed":0,"syntology":null},{"url":null,"slug":"runtime-performances-benchmark-for-knowledge","title":"Runtime Performances Benchmark for Knowledge Graph Embedding Methods","date":"2020-11-05","arxiv_id":"2011.04275","repositories_listed":0,"syntology":null},{"url":null,"slug":"gage-geometry-preserving-attributed-graph","title":"GAGE: Geometry Preserving Attributed Graph Embeddings","date":"2020-11-03","arxiv_id":"2011.01422","repositories_listed":0,"syntology":null},{"url":null,"slug":"gain-graph-attention-interaction-network-for","title":"GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs","date":"2020-11-03","arxiv_id":"2011.01393","repositories_listed":0,"syntology":null},{"url":null,"slug":"h2kgat-hierarchical-hyperbolic-knowledge","title":"H2KGAT: Hierarchical Hyperbolic Knowledge Graph Attention Network","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"recurrent-event-network-autoregressive","title":"Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"disene-disentangling-knowledge-graph","title":"DisenE: Disentangling Knowledge Graph Embeddings","date":"2020-10-28","arxiv_id":"2010.14730","repositories_listed":0,"syntology":null},{"url":null,"slug":"co-embedding-of-nodes-and-edges-with-graph","title":"Co-embedding of Nodes and Edges with Graph Neural Networks","date":"2020-10-25","arxiv_id":"2010.13242","repositories_listed":0,"syntology":null},{"url":null,"slug":"not-half-bad-exploring-half-precision-in","title":"Not Half Bad: Exploring Half-Precision in Graph Convolutional Neural Networks","date":"2020-10-23","arxiv_id":"2010.12635","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-shot-learning-for-temporal-knowledge","title":"One-shot Learning for Temporal Knowledge Graphs","date":"2020-10-23","arxiv_id":"2010.12144","repositories_listed":0,"syntology":null},{"url":null,"slug":"directed-graph-representation-through-vector","title":"Directed Graph Representation through Vector Cross Product","date":"2020-10-21","arxiv_id":"2010.10737","repositories_listed":0,"syntology":null},{"url":null,"slug":"biomedical-network-link-prediction-using","title":"Biomedical Network Link Prediction using Neural Network Graph Embedding","date":"2020-10-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"instantembedding-efficient-local-node-1","title":"InstantEmbedding: Efficient Local Node Representations","date":"2020-10-14","arxiv_id":"2010.06992","repositories_listed":0,"syntology":null},{"url":null,"slug":"mulde-multi-teacher-knowledge-distillation","title":"MulDE: Multi-teacher Knowledge Distillation for Low-dimensional Knowledge Graph Embeddings","date":"2020-10-14","arxiv_id":"2010.07152","repositories_listed":0,"syntology":null},{"url":null,"slug":"motif-learning-in-knowledge-graphsusing","title":"Motif Learning in Knowledge Graphs Using Trajectories Of Differential Equations","date":"2020-10-13","arxiv_id":"2010.06684","repositories_listed":0,"syntology":null},{"url":null,"slug":"rate-relation-adaptive-translating-embedding","title":"RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion","date":"2020-10-10","arxiv_id":"2010.04863","repositories_listed":0,"syntology":null},{"url":null,"slug":"conformal-retrofitting-via-riemannian","title":"Conformal retrofitting via Riemannian manifolds: distilling task-specific graphs into pretrained embeddings","date":"2020-10-09","arxiv_id":"2010.04842","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-semantics-and-data-driven-path","title":"Joint Semantics and Data-Driven Path Representation for Knowledge Graph Inference","date":"2020-10-06","arxiv_id":"2010.02602","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-positive-unlabeled-learning-for","title":"Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation","date":"2020-10-05","arxiv_id":"2010.01916","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-graph-embeddings-in-geometric","title":"Knowledge Graph Embeddings in Geometric Algebras","date":"2020-10-02","arxiv_id":"2010.00989","repositories_listed":0,"syntology":null},{"url":null,"slug":"rm-n-small-ode-s-small-ig-random-walk","title":"NodeSig: Binary Node Embeddings via Random Walk Diffusion","date":"2020-10-01","arxiv_id":"2010.00261","repositories_listed":0,"syntology":null}],"record_sha256":"0ae16a986c746515cec567b39ecb8f670012b9928bf64855d012738a5534f971","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}