{"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/15","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":15,"pages_in_order":20,"rows_per_page":100,"rows":[1401,1500],"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/14","next":"/task/link-prediction/papers/16","papers":[{"url":null,"slug":"learning-to-borrow-relation-representation","title":"Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"structure-enhanced-graph-neural-networks-for","title":"Structure Enhanced Graph Neural Networks for Link Prediction","date":"2022-01-14","arxiv_id":"2201.05293","repositories_listed":0,"syntology":null},{"url":null,"slug":"dehin-a-decentralized-framework-for-embedding","title":"DeHIN: A Decentralized Framework for Embedding Large-scale Heterogeneous Information Networks","date":"2022-01-08","arxiv_id":"2201.02757","repositories_listed":0,"syntology":null},{"url":null,"slug":"scaling-knowledge-graph-embedding-models","title":"Scaling Knowledge Graph Embedding Models","date":"2022-01-08","arxiv_id":"2201.02791","repositories_listed":0,"syntology":null},{"url":null,"slug":"neighbor2vec-an-efficient-and-effective","title":"Neighbor2vec: an efficient and effective method for Graph Embedding","date":"2022-01-07","arxiv_id":"2201.02626","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-dyn-dynamic-graph-representation","title":"Sparse-Dyn: Sparse Dynamic Graph Multi-representation Learning via Event-based Sparse Temporal Attention Network","date":"2022-01-04","arxiv_id":"2201.01384","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-collaborative-reasoning","title":"Graph Collaborative Reasoning","date":"2021-12-27","arxiv_id":"2112.13705","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-analytical-survey-on","title":"A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods","date":"2021-12-20","arxiv_id":"2112.10372","repositories_listed":0,"syntology":null},{"url":null,"slug":"kgboost-a-classification-based-knowledge-base","title":"KGBoost: A Classification-based Knowledge Base Completion Method with Negative Sampling","date":"2021-12-17","arxiv_id":"2112.09340","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-graph-embedding-in-e-commerce","title":"Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules","date":"2021-12-16","arxiv_id":"2112.08589","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-dynamic-graph-representation","title":"Self-Supervised Dynamic Graph Representation Learning via Temporal Subgraph Contrast","date":"2021-12-16","arxiv_id":"2112.08733","repositories_listed":0,"syntology":null},{"url":null,"slug":"neighborhood-random-walk-graph-sampling-for","title":"Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks","date":"2021-12-14","arxiv_id":"2112.07743","repositories_listed":0,"syntology":null},{"url":null,"slug":"trivial-bundle-embeddings-for-learning-graph","title":"Trivial bundle embeddings for learning graph representations","date":"2021-12-05","arxiv_id":"2112.02531","repositories_listed":0,"syntology":null},{"url":null,"slug":"alx-large-scale-matrix-factorization-on-tpus","title":"ALX: Large Scale Matrix Factorization on TPUs","date":"2021-12-03","arxiv_id":"2112.02194","repositories_listed":0,"syntology":null},{"url":null,"slug":"enginekgi-closed-loop-knowledge-graph","title":"Perform Like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference","date":"2021-12-02","arxiv_id":"2112.01040","repositories_listed":0,"syntology":null},{"url":null,"slug":"structure-aware-random-fourier-kernel-for","title":"Structure-Aware Random Fourier Kernel for Graphs","date":"2021-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mdistmult-a-multiple-scoring-functions-model","title":"MDistMult: A Multiple Scoring Functions Model for Link Prediction on Antiviral Drugs Knowledge Graph","date":"2021-11-29","arxiv_id":"2111.14480","repositories_listed":0,"syntology":null},{"url":"/paper/network-in-graph-neural-network","slug":"network-in-graph-neural-network","title":"Network In Graph Neural Network","date":"2021-11-23","arxiv_id":"2111.11638","repositories_listed":0,"syntology":null},{"url":null,"slug":"triple-classification-for-scholarly-knowledge","title":"Triple Classification for Scholarly Knowledge Graph Completion","date":"2021-11-23","arxiv_id":"2111.11845","repositories_listed":0,"syntology":null},{"url":null,"slug":"citation-network-applications-in-a-scientific","title":"Citation network applications in a scientific co-authorship recommender system","date":"2021-11-22","arxiv_id":"2111.15466","repositories_listed":0,"syntology":null},{"url":"/paper/do-pre-trained-models-benefit-knowledge-graph","slug":"do-pre-trained-models-benefit-knowledge-graph","title":"Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"prix-lm-pretraining-for-multilingual-1","title":"Prix-LM: Pretraining for Multilingual Knowledge Base Construction","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sequence-to-sequence-knowledge-graph","title":"Sequence-to-Sequence Knowledge Graph Completion and Question Answering","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"star-knowledge-graph-embedding-by-scaling","title":"STaR: Knowledge Graph Embedding by Scaling, Translation and Rotation","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-knowledge-graph-embedding-based-on","title":"Temporal Knowledge Graph Embedding based on Multivariate Gaussian Process","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"symmetry-driven-link-prediction-in-networks","title":"Symmetry-driven network reconstruction through pseudobalanced coloring optimization","date":"2021-11-15","arxiv_id":"2111.07821","repositories_listed":0,"syntology":null},{"url":null,"slug":"topic-aware-latent-models-for-representation","title":"Topic-aware latent models for representation learning on networks","date":"2021-11-10","arxiv_id":"2111.05576","repositories_listed":0,"syntology":null},{"url":null,"slug":"growl-group-detection-with-link-prediction","title":"GROWL: Group Detection With Link Prediction","date":"2021-11-08","arxiv_id":"2111.04397","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-order-joint-embedding-for-multi-level","title":"High-order joint embedding for multi-level link prediction","date":"2021-11-07","arxiv_id":"2111.05265","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-probit-tensor-factorization-model-for","title":"A Probit Tensor Factorization Model For Relational Learning","date":"2021-11-06","arxiv_id":"2111.03943","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-interpretable-graph-generative-model-with-1","title":"Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings","date":"2021-11-04","arxiv_id":"2111.03030","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-semantic-filter-based-on-relations-for","title":"A Semantic Filter Based on Relations for Knowledge Graph Completion","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperbolic-hierarchy-aware-knowledge-graph","title":"Hyperbolic Hierarchy-Aware Knowledge Graph Embedding for Link Prediction","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-graph-representation-learning-using","title":"Knowledge Graph Representation Learning using Ordinary Differential Equations","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"low-resource-quadratic-forms-for-knowledge","title":"Low Resource Quadratic Forms for Knowledge Graph Embeddings","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-embedding-with-hierarchical-attentive","title":"Graph Embedding with Hierarchical Attentive Membership","date":"2021-10-31","arxiv_id":"2111.00604","repositories_listed":0,"syntology":null},{"url":null,"slug":"higher-order-relations-skew-link-prediction","title":"Higher-Order Relations Skew Link Prediction in Graphs","date":"2021-10-30","arxiv_id":"2111.00271","repositories_listed":0,"syntology":null},{"url":null,"slug":"love-thy-neighbour-remeasuring-local","title":"Love tHy Neighbour: Remeasuring Local Structural Node Similarity in Hypergraph-Derived Networks","date":"2021-10-30","arxiv_id":"2111.00256","repositories_listed":0,"syntology":null},{"url":null,"slug":"barlow-graph-auto-encoder-for-unsupervised","title":"Barlow Graph Auto-Encoder for Unsupervised Network Embedding","date":"2021-10-29","arxiv_id":"2110.15742","repositories_listed":0,"syntology":null},{"url":"/paper/tackling-oversmoothing-of-gnns-with-1","slug":"tackling-oversmoothing-of-gnns-with-1","title":"Deeper-GXX: Deepening Arbitrary GNNs","date":"2021-10-26","arxiv_id":"2110.13798","repositories_listed":0,"syntology":null},{"url":null,"slug":"degree-based-random-walk-approach-for-graph","title":"Degree-Based Random Walk Approach for Graph Embedding","date":"2021-10-21","arxiv_id":"2110.13627","repositories_listed":0,"syntology":null},{"url":null,"slug":"drug-re-positioning-via-text-augmented","title":"Drug Re-positioning via Text Augmented Knowledge Graph Embeddings","date":"2021-10-20","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"why-settle-for-just-one-extending-el-ontology","title":"Why Settle for Just One? Extending EL++ Ontology Embeddings with Many-to-Many Relationships","date":"2021-10-20","arxiv_id":"2110.10555","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-partner-neural-networks-for-semi","title":"Graph Partner Neural Networks for Semi-Supervised Learning on Graphs","date":"2021-10-18","arxiv_id":"2110.09182","repositories_listed":0,"syntology":null},{"url":null,"slug":"network-representation-learning-from-1","title":"Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding","date":"2021-10-14","arxiv_id":"2110.07582","repositories_listed":0,"syntology":null},{"url":null,"slug":"dyn-backdoor-backdoor-attack-on-dynamic-link","title":"Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction","date":"2021-10-08","arxiv_id":"2110.03875","repositories_listed":0,"syntology":null},{"url":null,"slug":"simplicial-convolutional-neural-networks","title":"Simplicial Convolutional Neural Networks","date":"2021-10-06","arxiv_id":"2110.02585","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-network-embedding-via-adversarial-auto","title":"Latent Network Embedding via Adversarial Auto-encoders","date":"2021-09-30","arxiv_id":"2109.15257","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-latent-space-model-for-directed-graph","title":"A Deep Latent Space Model for Directed Graph Representation Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-learning-of-probabilistic","title":"End-to-End Learning of Probabilistic Hierarchies on Graphs","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"equivariant-heterogeneous-graph-networks","title":"Equivariant Heterogeneous Graph Networks","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-automatic-hypothesis-generation","title":"Explainable Automatic Hypothesis Generation via High-order Graph Walks","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"explaining-knowledge-graph-embedding-via","title":"Explaining Knowledge Graph Embedding via Latent Rule Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-graph-link-prediction-with-domain","title":"Few-shot graph link prediction with domain adaptation","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-level-approach-to-accurate-and-scalable","title":"MULTI-LEVEL APPROACH TO ACCURATE AND SCALABLE HYPERGRAPH EMBEDDING","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"online-graph-nets","title":"Online graph nets","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-virtual-nodes-in-graph-neural","title":"Revisiting Virtual Nodes in Graph Neural Networks for Link Prediction","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-hierarchical-embeddings-of-complex","title":"Scalable Hierarchical Embeddings of Complex Networks","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"contig-continuous-representation-learning-on","title":"ConTIG: Continuous Representation Learning on Temporal Interaction Graphs","date":"2021-09-27","arxiv_id":"2110.06088","repositories_listed":0,"syntology":null},{"url":null,"slug":"updating-embeddings-for-dynamic-knowledge","title":"Updating Embeddings for Dynamic Knowledge Graphs","date":"2021-09-22","arxiv_id":"2109.10896","repositories_listed":0,"syntology":null},{"url":null,"slug":"mgnn-generalizing-the-graph-neural-networks","title":"mGNN: Generalizing the Graph Neural Networks to the Multilayer Case","date":"2021-09-21","arxiv_id":"2109.10119","repositories_listed":0,"syntology":null},{"url":null,"slug":"wsgat-weighted-and-signed-graph-attention","title":"wsGAT: Weighted and Signed Graph Attention Networks for Link Prediction","date":"2021-09-21","arxiv_id":"2109.11519","repositories_listed":0,"syntology":null},{"url":null,"slug":"harnessing-the-power-of-ego-network-layers","title":"Harnessing the Power of Ego Network Layers for Link Prediction in Online Social Networks","date":"2021-09-19","arxiv_id":"2109.09190","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-variational-graph-autoencoders-for","title":"Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chains","date":"2021-09-17","arxiv_id":"2109.08722","repositories_listed":0,"syntology":null},{"url":null,"slug":"r-gat-relational-graph-attention-network-for","title":"r-GAT: Relational Graph Attention Network for Multi-Relational Graphs","date":"2021-09-13","arxiv_id":"2109.05922","repositories_listed":0,"syntology":null},{"url":null,"slug":"ergodic-limits-relaxations-and-geometric","title":"Ergodic Limits, Relaxations, and Geometric Properties of Random Walk Node Embeddings","date":"2021-09-09","arxiv_id":"2109.04526","repositories_listed":0,"syntology":null},{"url":null,"slug":"quint-node-embedding-using-network-hashing","title":"QUINT: Node embedding using network hashing","date":"2021-09-09","arxiv_id":"2109.04206","repositories_listed":0,"syntology":null},{"url":null,"slug":"hmsg-heterogeneous-graph-neural-network-based","title":"HMSG: Heterogeneous Graph Neural Network based on Metapath Subgraph Learning","date":"2021-09-07","arxiv_id":"2109.02868","repositories_listed":0,"syntology":null},{"url":null,"slug":"job-posting-enriched-knowledge-graph-for","title":"Job Posting-Enriched Knowledge Graph for Skills-based Matching","date":"2021-09-06","arxiv_id":"2109.02554","repositories_listed":0,"syntology":null},{"url":null,"slug":"discussion-structure-prediction-based-on-a","title":"Discussion Structure Prediction Based on a Two-step Method","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"heterogeneous-graph-neural-network-with-multi","title":"Heterogeneous Graph Neural Network with Multi-view Representation Learning","date":"2021-08-31","arxiv_id":"2108.13650","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-transductive-and-inductive","title":"Integrating Transductive And Inductive Embeddings Improves Link Prediction Accuracy","date":"2021-08-23","arxiv_id":"2108.10108","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-network-embedding-via-tensor","title":"Temporal Network Embedding via Tensor Factorization","date":"2021-08-22","arxiv_id":"2108.09837","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-network-embedding-with","title":"Semi-supervised Network Embedding with Differentiable Deep Quantisation","date":"2021-08-20","arxiv_id":"2108.09128","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptive-hash-for","title":"Unsupervised Domain-adaptive Hash for Networks","date":"2021-08-20","arxiv_id":"2108.09136","repositories_listed":0,"syntology":null},{"url":null,"slug":"rrlfsor-an-efficient-self-supervised-learning","title":"RRLFSOR: An Efficient Self-Supervised Learning Strategy of Graph Convolutional Networks","date":"2021-08-17","arxiv_id":"2108.07481","repositories_listed":0,"syntology":null},{"url":null,"slug":"span-subgraph-prediction-attention-network","title":"SPAN: Subgraph Prediction Attention Network for Dynamic Graphs","date":"2021-08-17","arxiv_id":"2108.07776","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-model-integration-algorithm-for","title":"Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks","date":"2021-08-03","arxiv_id":"2108.01532","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-faithful-representations-of-causal","title":"Learning Faithful Representations of Causal Graphs","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"subgraph-aware-few-shot-inductive-link","title":"Subgraph-aware Few-Shot Inductive Link Prediction via Meta-Learning","date":"2021-07-26","arxiv_id":"2108.00954","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-representation-learning-on-tissue","title":"Graph Representation Learning on Tissue-Specific Multi-Omics","date":"2021-07-25","arxiv_id":"2107.11856","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-considerations-in-graph-representation","title":"Data Considerations in Graph Representation Learning for Supply Chain Networks","date":"2021-07-22","arxiv_id":"2107.10609","repositories_listed":0,"syntology":null},{"url":null,"slug":"technical-report-of-team-graphmiracles-in-the","title":"Technical Report of Team GraphMIRAcles in the WikiKG90M-LSC Track of OGB-LSC @ KDD Cup 2021","date":"2021-07-12","arxiv_id":"2107.05476","repositories_listed":0,"syntology":null},{"url":null,"slug":"patentminer-patent-vacancy-mining-via-context","title":"PatentMiner: Patent Vacancy Mining via Context-enhanced and Knowledge-guided Graph Attention","date":"2021-07-10","arxiv_id":"2107.04880","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-graph-learning-via-population-based","title":"Automated Graph Learning via Population Based Self-Tuning GCN","date":"2021-07-09","arxiv_id":"2107.04713","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-robustness-of-probabilistic","title":"Adversarial Robustness of Probabilistic Network Embedding for Link Prediction","date":"2021-07-05","arxiv_id":"2107.01936","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-representation-learning-on","title":"Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective","date":"2021-07-03","arxiv_id":"2107.01475","repositories_listed":0,"syntology":null},{"url":null,"slug":"trans4e-link-prediction-on-scholarly","title":"Trans4E: Link Prediction on Scholarly Knowledge Graphs","date":"2021-07-03","arxiv_id":"2107.03297","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-space-model-for-higher-order-networks","title":"Latent Space Model for Higher-order Networks and Generalized Tensor Decomposition","date":"2021-06-30","arxiv_id":"2106.16042","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-static-models-for-link-prediction","title":"Leveraging Static Models for Link Prediction in Temporal Knowledge Graphs","date":"2021-06-29","arxiv_id":"2106.15223","repositories_listed":0,"syntology":null},{"url":"/paper/kgrefiner-knowledge-graph-refinement-for","slug":"kgrefiner-knowledge-graph-refinement-for","title":"KGRefiner: Knowledge Graph Refinement for Improving Accuracy of Translational Link Prediction Methods","date":"2021-06-27","arxiv_id":"2106.14233","repositories_listed":0,"syntology":null},{"url":null,"slug":"behavioral-testing-of-knowledge-graph","title":"Behavioral Testing of Knowledge Graph Embedding Models for Link Prediction","date":"2021-06-22","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-network-embedding-in-apache-spark","title":"Large-Scale Network Embedding in Apache Spark","date":"2021-06-20","arxiv_id":"2106.10620","repositories_listed":0,"syntology":null},{"url":null,"slug":"embedding-heterogeneous-networks-into","title":"Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path","date":"2021-06-18","arxiv_id":"2106.09923","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-attract-repel-decomposition-of-undirected","title":"Pseudo-Euclidean Attract-Repel Embeddings for Undirected Graphs","date":"2021-06-17","arxiv_id":"2106.09671","repositories_listed":0,"syntology":null},{"url":null,"slug":"coane-modeling-context-co-occurrence-for","title":"CoANE: Modeling Context Co-occurrence for Attributed Network Embedding","date":"2021-06-17","arxiv_id":"2106.09241","repositories_listed":0,"syntology":null},{"url":null,"slug":"directed-graph-embeddings-in-pseudo","title":"Directed Graph Embeddings in Pseudo-Riemannian Manifolds","date":"2021-06-16","arxiv_id":"2106.08678","repositories_listed":0,"syntology":null},{"url":null,"slug":"node-classification-meets-link-prediction-on","title":"Node Classification Meets Link Prediction on Knowledge Graphs","date":"2021-06-14","arxiv_id":"2106.07297","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-symbiosis-learning","title":"AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange","date":"2021-06-10","arxiv_id":"2106.05455","repositories_listed":0,"syntology":null},{"url":null,"slug":"vertex-centric-visual-programming-for-graph","title":"Vertex-Centric Visual Programming for Graph Neural Networks","date":"2021-06-09","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"893c308193590e9f07c76df9f06e1d481f71002364f3746019dbab060f897faf","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}