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 1851–1875 of 1949 papers

TitleStatusHype
Scientific Paper Extractive Summarization Enhanced by Citation Graphs—0
Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach—0
A Survey on Graph Classification and Link Prediction based on GNN—0
Hyperbolic Hierarchy-Aware Knowledge Graph Embedding for Link Prediction—0
Hyperbolic Temporal Knowledge Graph Embeddings with Relational and Time Curvatures—0
HyperCausalLP: Causal Link Prediction using Hyper-Relational Knowledge Graph—0
A Survey on Embedding Dynamic Graphs—0
Seastar: vertex-centric programming for graph neural networks—0
HyperKG: Hyperbolic Knowledge Graph Embeddings for Knowledge Base Completion—0
Hyperlink Regression via Bregman Divergence—0
A Survey of Link Prediction in Temporal Networks—0
A statistical interpretation of spectral embedding: the generalised random dot product graph—0
HyperQuery: Beyond Binary Link Prediction—0
Association Rules Enhanced Knowledge Graph Attention Network—0
Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks—0
Hypotheses generation using link prediction in a bipartite graph—0
Do We Really Need Complicated Model Architectures For Temporal Networks?—0
Dot Product is All You Need: Bridging the Gap Between Item Recommendation and Link Prediction—0
Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach—0
Improved Deep Embeddings for Inferencing with Multi-Layered Networks—0
Improved Knowledge Base Completion by Path-Augmented TransR Model—0
Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment—0
AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks—0
Improving global awareness of linkset predictions using Cross-Attentive Modulation tokens—0
Select and Augment: Enhanced Dense Retrieval Knowledge Graph Augmentation—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