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

Showing 17761800 of 1949 papers

TitleStatusHype
Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link PredictionCode0
Network Representation Learning: Consolidation and Renewed BearingCode0
Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress?Code0
Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network EmbeddingCode0
Neural Collaborative Filtering vs. Matrix Factorization RevisitedCode0
Neural Concept Formation in Knowledge GraphsCode0
Neural Graph Embedding Methods for Natural Language ProcessingCode0
Towards Neural Scaling Laws on GraphsCode0
Neural Subgraph Isomorphism CountingCode0
Neural Variational Inference For Estimating Uncertainty in Knowledge Graph EmbeddingsCode0
New Perspectives on the Evaluation of Link Prediction Algorithms for Dynamic GraphsCode0
NNKGC: Improving Knowledge Graph Completion with Node NeighborhoodsCode0
Node Attribute Completion in Knowledge Graphs with Multi-Relational PropagationCode0
Node Duplication Improves Cold-start Link PredictionCode0
Node Embedding over Temporal GraphsCode0
Node Embedding with Adaptive Similarities for Scalable Learning over GraphsCode0
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood PredictionCode0
Scalable and Efficient Temporal Graph Representation Learning via Forward Recent SamplingCode0
Normed Spaces for Graph EmbeddingCode0
Novel Node Category Detection Under Subpopulation ShiftCode0
Numerical Literals in Link Prediction: A Critical Examination of Models and DatasetsCode0
Obtaining Dyadic Fairness by Optimal TransportCode0
OKGIT: Open Knowledge Graph Link Prediction with Implicit TypesCode0
OKGIT: Open Knowledge Graph Link Prediction with Implicit TypesCode0
On Calibration of Graph Neural Networks for Node ClassificationCode0
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Benchmark Results

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