SOTAVerified

Link Prediction

Link Prediction is a task in graph and network analysis where the goal is to predict missing or future connections between nodes in a network. Given a partially observed network, the goal of link prediction is to infer which links are most likely to be added or missing based on the observed connections and the structure of the network.

( Image credit: Inductive Representation Learning on Large Graphs )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1426–1450 of 1949 papers

TitleStatusHype
Smoothing Graphons for Modelling Exchangeable Relational Data—0
Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress?Code0
End-to-end Emotion-Cause Pair Extraction via Learning to LinkCode1
Recurrent Dirichlet Belief Networks for Interpretable Dynamic Relational Data Modelling—0
Data Augmentation for Personal Knowledge Base Population—0
Inductive Representation Learning on Temporal GraphsCode1
Investigating Extensions to Random Walk Based Graph Embedding—0
On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction MethodsCode0
Block-Approximated Exponential Random GraphsCode0
Graph Convolutional Gaussian Processes For Link Prediction—0
Vertex-reinforced Random Walk for Network EmbeddingCode0
Pre-training Tasks for Embedding-based Large-scale Retrieval—0
Learning graph representations of biochemical networks and its application to enzymatic link predictionCode0
Message Passing Query EmbeddingCode1
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph EmbeddingCode1
Graph Representation Learning via Graphical Mutual Information MaximizationCode1
ALPINE: Active Link Prediction using Network Embedding—0
Knowledge Graph Embedding for Link Prediction: A Comparative AnalysisCode1
Which way? Direction-Aware Attributed Graph EmbeddingCode0
How Much and When Do We Need Higher-order Information in Hypergraphs? A Case Study on Hyperedge Prediction—0
Tri-graph Information Propagation for Polypharmacy Side Effect PredictionCode1
Graph Neighborhood Attentive PoolingCode0
Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data—0
Simple and Effective Graph Autoencoders with One-Hop Linear ModelsCode1
Graph Ordering: Towards the Optimal by Learning—0
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All datasetsFB15k-237WN18RRWN18FB15kYAGO3-10ICEWS05-15ICEWS14CiteseerCoraPubmedWikidata5MGDELT

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AutoKGEHits@100.56—Unverified
2CP-N3-RPHits@100.55—Unverified
3DistMult (after variational EM)Hits@100.55—Unverified
4KG-R3Hits@100.54—Unverified
5LASSHits@100.53—Unverified
6MDE_advHits@100.53—Unverified
7GFA-NNHits@100.52—Unverified
8KGRefinerHits@100.49—Unverified
9ComplEx NSCachingHits@100.48—Unverified
10LogicENNHits@100.47—Unverified
#ModelMetricClaimedVerifiedStatus
1MoCoKGCHits@100.88—Unverified
2KERMITHits@100.83—Unverified
3MoCoSAHits@100.82—Unverified
4SimKGCIB(+PB+SN)Hits@100.82—Unverified
5C-LMKE(bert-base)Hits@100.79—Unverified
6LASSHits@100.79—Unverified
7LP-BERTHits@100.75—Unverified
8KGLMHits@100.74—Unverified
9StAR(Self-Adp)Hits@100.71—Unverified
10PALTHits@100.69—Unverified
#ModelMetricClaimedVerifiedStatus
1OpenKE (han2018openke)training time (s)11—Unverified
2LibKGE (ruffinelli2020you)training time (s)10—Unverified
3GraphVite (zhu2019graphvite)training time (s)6—Unverified
4Inverse ModelHits@100.96—Unverified
5QuatDEHits@100.96—Unverified
6LineaREHits@100.96—Unverified
7AutoKGEHits@100.96—Unverified
8MEI (small)Hits@100.96—Unverified
9ComplEx-N3 (reciprocal)Hits@100.96—Unverified
10RotatEHits@100.96—Unverified
#ModelMetricClaimedVerifiedStatus
1OPTransEHits@100.9—Unverified
2AutoKGEMRR0.86—Unverified
3ComplEx-N3 (reciprocal)MRR0.86—Unverified
4LineaREMRR0.84—Unverified
5DistMult (after variational EM)MRR0.84—Unverified
6QuatEMRR0.83—Unverified
7SEEKMRR0.83—Unverified
8MEI-BTDMRR0.81—Unverified
9MEI (small)MRR0.8—Unverified
10pRotatEMRR0.8—Unverified