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 1776–1800 of 1949 papers

TitleStatusHype
Scalable Deep Metric Learning on Attributed Graphs—0
H2KGAT: Hierarchical Hyperbolic Knowledge Graph Attention Network—0
A Critical Examination of RESCAL for Completion of Knowledge Bases with Transitive Relations—0
HAGNN: Hybrid Aggregation for Heterogeneous Graph Neural Networks—0
Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction—0
Halting in Random Walk Kernels—0
Handling Class Imbalance in Link Prediction using Learning to Rank Techniques—0
Handling oversampling in dynamic networks using link prediction—0
Dynamic Network Embeddings for Network Evolution Analysis—0
Harmonizing Human Insights and AI Precision: Hand in Hand for Advancing Knowledge Graph Task—0
Harnessing the Power of Ego Network Layers for Link Prediction in Online Social Networks—0
Harvesting Textual and Structured Data from the HAL Publication Repository—0
Dynamic Link Prediction for New Nodes in Temporal Graph Networks—0
Scalable Hierarchical Embeddings of Complex Networks—0
Dynamic Link and Flow Prediction in Bank Transfer Networks—0
Heterogeneous Edge Embeddings for Friend Recommendation—0
Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning—0
Heterogeneous Graph based Deep Learning for Biomedical Network Link Prediction—0
Heterogeneous Graph Neural Network with Multi-view Representation Learning—0
Dynamic Joint Variational Graph Autoencoders—0
HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs—0
Heterophilic Graph Neural Networks Optimization with Causal Message-passing—0
HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning—0
HetFS: A Method for Fast Similarity Search with Ad-hoc Meta-paths on Heterogeneous Information Networks—0
Heuristic-Informed Mixture of Experts for Link Prediction in Multilayer Networks—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