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 126–150 of 1949 papers

TitleStatusHype
One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs—0
Attribute-Enhanced Similarity Ranking for Sparse Link Prediction—0
Integrating Social Determinants of Health into Knowledge Graphs: Evaluating Prediction Bias and Fairness in HealthcareCode0
ContextGNN: Beyond Two-Tower Recommendation SystemsCode1
Bridging AI and Science: Implications from a Large-Scale Literature Analysis of AI4ScienceCode0
Perturbation Ontology based Graph Attention Networks—0
Deep Sparse Latent Feature Models for Knowledge Graph Completion—0
TANGNN: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism for Graph Representation LearningCode0
Can GNNs Learn Link Heuristics? A Concise Review and Evaluation of Link Prediction MethodsCode0
VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models—0
Heterophilic Graph Neural Networks Optimization with Causal Message-passing—0
Scalable Deep Metric Learning on Attributed Graphs—0
Hierarchical-Graph-Structured Edge Partition Models for Learning Evolving Community Structure—0
MoCoKGC: Momentum Contrast Entity Encoding for Knowledge Graph Completion—0
Enhancing Link Prediction with Fuzzy Graph Attention Networks and Dynamic Negative Sampling—0
Shedding Light on Problems with Hyperbolic Graph Learning—0
HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning—0
UniHR: Hierarchical Representation Learning for Unified Knowledge Graph Link PredictionCode1
Discovering emergent connections in quantum physics research via dynamic word embeddingsCode0
YOSO: You-Only-Sample-Once via Compressed Sensing for Graph Neural Network Training—0
Reconsidering the Performance of GAE in Link PredictionCode1
Enhancing the Expressivity of Temporal Graph Networks through Source-Target IdentificationCode0
PageRank Bandits for Link PredictionCode0
G-SPARC: SPectral ARchitectures tackling the Cold-start problem in Graph learning—0
An unified approach to link prediction in collaboration networksCode0
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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
8ComplEx-N3 (reciprocal)Hits@100.96—Unverified
9MEI (small)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