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 1576–1600 of 1949 papers

TitleStatusHype
Representation Learning for Scale-free Networks—0
Representation learning of dynamic networks—0
Representation Learning of Graphs Using Graph Convolutional Multilayer Networks Based on Motifs—0
Representation Learning of Reconstructed Graphs Using Random Walk Graph Convolutional Network—0
Erratum: Link prediction in drug-target interactions network using similarity indices—0
Explainable Automatic Hypothesis Generation via High-order Graph Walks—0
Representation Learning over Dynamic Graphs—0
Explainable Link Prediction for Privacy-Preserving Contact Tracing—0
Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs—0
Explaining Dynamic Graph Neural Networks via Relevance Back-propagation—0
Explaining Knowledge Graph Embedding via Latent Rule Learning—0
Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering Over Knowledge Graphs—0
GS-KGC: A Generative Subgraph-based Framework for Knowledge Graph Completion with Large Language Models—0
ResVGAE: Going Deeper with Residual Modules for Link Prediction—0
TPGNN: Learning High-order Information in Dynamic Graphs via Temporal Propagation—0
Representing Text for Joint Embedding of Text and Knowledge Bases—0
t-PINE: Tensor-based Predictable and Interpretable Node Embeddings—0
Exploring Effects of Random Walk Based Minibatch Selection Policy on Knowledge Graph Completion—0
Exploring Graph Neural Networks for Indian Legal Judgment Prediction—0
Ergodic Limits, Relaxations, and Geometric Properties of Random Walk Node Embeddings—0
Pre-Trained Models for Heterogeneous Information Networks—0
Causal Intervention for Measuring Confidence in Drug-Target Interaction Prediction—0
Equivariant Heterogeneous Graph Networks—0
Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs—0
Adaptive Convolution for Multi-Relational 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