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 501–525 of 1949 papers

TitleStatusHype
A Bayesian Tensor Factorization Model via Variational Inference for Link Prediction—0
Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction—0
A Probit Tensor Factorization Model For Relational Learning—0
A Novel Graph Transformer Framework for Gene Regulatory Network Inference—0
Gaussian Process Models for Link Analysis and Transfer Learning—0
GNN Applied to Ego-nets for Friend Suggestions—0
DualDE: Dually Distilling Knowledge Graph Embedding for Faster and Cheaper Reasoning—0
A Discriminative Topic Model using Document Network Structure—0
Edge2Node: Reducing Edge Prediction to Node Classification—0
BERT4FCA: A Method for Bipartite Link Prediction using Formal Concept Analysis and BERT—0
GANExplainer: GAN-based Graph Neural Networks Explainer—0
GCN-ALP: Addressing Matching Collisions in Anchor Link Prediction—0
Generating Human Understandable Explanations for Node Embeddings—0
GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint—0
Graph Attention Inference of Network Topology in Multi-Agent Systems—0
Disentangling Node Attributes from Graph Topology for Improved Generalizability in Link Prediction—0
From Wide to Deep: Dimension Lifting Network for Parameter-efficient Knowledge Graph Embedding—0
Disentangling homophily, community structure and triadic closure in networks—0
Disentangled Hyperbolic Representation Learning for Heterogeneous Graphs—0
A Differential Geometric View and Explainability of GNN on Evolving Graphs—0
DisenE: Disentangling Knowledge Graph Embeddings—0
Benchmarking Retrieval-Augmented Large Language Models in Biomedical NLP: Application, Robustness, and Self-Awareness—0
Discussion Structure Prediction Based on a Two-step Method—0
From Link Prediction to Forecasting: Information Loss in Batch-based Temporal Graph Learning—0
From One Point to A Manifold: Knowledge Graph Embedding For Precise Link Prediction—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