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 1626–1650 of 1949 papers

TitleStatusHype
Relation Embedding with Dihedral Group in Knowledge Graph—0
Knowledge Hypergraphs: Prediction Beyond Binary RelationsCode0
Abstract Graphs and Abstract Paths for Knowledge Graph Completion—0
Adaptive Convolution for Multi-Relational Learning—0
End to end learning and optimization on graphsCode0
Graph Learning Network: A Structure Learning AlgorithmCode0
STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender SystemsCode0
FOBE and HOBE: First- and High-Order Bipartite Embeddings—0
Is a Single Vector Enough? Exploring Node Polysemy for Network EmbeddingCode0
MDE: Multiple Distance Embeddings for Link Prediction in Knowledge GraphsCode0
Spring-Electrical Models For Link PredictionCode0
Multi-relational Poincaré Graph EmbeddingsCode0
GLEE: Geometric Laplacian Eigenmap EmbeddingCode0
Knowledge Graph Embedding Bi-Vector Models for Symmetric Relation—0
Gravity-Inspired Graph Autoencoders for Directed Link PredictionCode0
Neural-Symbolic Argumentation Mining: an Argument in Favor of Deep Learning and Reasoning—0
Mutual Information Maximization in Graph Neural NetworksCode0
LogicENN: A Neural Based Knowledge Graphs Embedding Model with Logical Rules—0
TuckER: Tensor Factorization for Knowledge Graph CompletionCode0
Relation Structure-Aware Heterogeneous Information Network Embedding—0
Stochastic Blockmodels meet Graph Neural Networks—0
Can NetGAN be improved on short random walks?Code0
Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social ContextsCode0
Network Representation Learning: Consolidation and Renewed BearingCode0
Investigating Robustness and Interpretability of Link Prediction via Adversarial ModificationsCode0
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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