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
SimplE Embedding for Link Prediction in Knowledge GraphsCode0
Weakly supervised collective feature learning from curated media—0
M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search—0
A Generative Model for Dynamic Networks with Applications—0
Beyond Markov Logic: Efficient Mining of Prediction Rules in Large GraphsCode0
Incorporating Literals into Knowledge Graph EmbeddingsCode0
Modeling polypharmacy side effects with graph convolutional networksCode0
Knowledge Graph Embedding with Multiple Relation Projections—0
Fast Node Embeddings: Learning Ego-Centric Representations—0
Revisiting Knowledge Base Embedding as Tensor Decomposition—0
GraphGAN: Generating Graphs via Random Walks—0
Hypotheses generation using link prediction in a bipartite graph—0
Predicting Rich Drug-Drug Interactions via Biomedical Knowledge Graphs and Text Jointly Embedding—0
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural NetworkCode0
SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link PredictionCode0
PRUNE: Preserving Proximity and Global Ranking for Network EmbeddingCode0
Knowledge Graph Embedding with Iterative Guidance from Soft RulesCode0
Representation Learning for Scale-free Networks—0
GraphGAN: Graph Representation Learning with Generative Adversarial NetsCode0
Adversarial Network Embedding—0
TorusE: Knowledge Graph Embedding on a Lie Group—0
On the ERM Principle with Networked Data—0
Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization PerspectiveCode0
KBGAN: Adversarial Learning for Knowledge Graph EmbeddingsCode0
Open-World Knowledge Graph CompletionCode0
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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