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 1151–1175 of 1949 papers

TitleStatusHype
Scaling up Dynamic Edge Partition Models via Stochastic Gradient MCMC—0
Schema-Guided Event Graph Completion—0
Scientific Discovery as Link Prediction in Influence and Citation Graphs—0
Scientific Paper Extractive Summarization Enhanced by Citation Graphs—0
Seastar: vertex-centric programming for graph neural networks—0
Select and Augment: Enhanced Dense Retrieval Knowledge Graph Augmentation—0
Self-Explainable Graph Neural Networks for Link Prediction—0
Self-Supervised Dynamic Graph Representation Learning via Temporal Subgraph Contrast—0
Self-Supervised Graph Representation Learning via Global Context Prediction—0
A Relation-Interactive Approach for Message Passing in Hyper-relational Knowledge Graphs—0
Self-supervised Learning for Heterogeneous Graph via Structure Information based on Metapath—0
Self-Supervised Pretraining for Heterogeneous Hypergraph Neural Networks—0
Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models—0
Semantically Smooth Knowledge Graph Embedding—0
Semantic Entity Enrichment by Leveraging Multilingual Descriptions for Link Prediction—0
Semi-supervised Graph Embedding Approach to Dynamic Link Prediction—0
Semi-supervised Network Embedding with Differentiable Deep Quantisation—0
Semi-supervised Training for Knowledge Base Graph Self-attention Networks on Link Prediction—0
SENSE: Semantically Enhanced Node Sequence Embedding—0
Sequence-to-Sequence Knowledge Graph Completion and Question Answering—0
SF-GNN: Self Filter for Message Lossless Propagation in Deep Graph Neural Network—0
Shedding Light on Problems with Hyperbolic Graph Learning—0
Dot Product is All You Need: Bridging the Gap Between Item Recommendation and Link Prediction—0
SigGAN : Adversarial Model for Learning Signed Relationships in Networks—0
Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings—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