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 1326–1350 of 1949 papers

TitleStatusHype
Unveiling Hidden Links Between Unseen Security Entities—0
Updating Embeddings for Dynamic Knowledge Graphs—0
Adaptive Pseudo-Siamese Policy Network for Temporal Knowledge Prediction—0
Using Adamic-Adar Index Algorithm to Predict Volunteer Collaboration: Less is More—0
Using Pairwise Occurrence Information to Improve Knowledge Graph Completion on Large-Scale Datasets—0
UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs—0
Utilizing Graph Neural Networks for Effective Link Prediction in Microservice Architectures—0
UWN: A Large Multilingual Lexical Knowledge Base—0
Variational Disentangled Graph Auto-Encoders for Link Prediction—0
Variational Knowledge Graph Reasoning—0
V-Coder: Adaptive AutoEncoder for Semantic Disclosure in Knowledge Graphs—0
Vertex-Centric Visual Programming for Graph Neural Networks—0
Embedding Cardinality Constraints in Neural Link Predictors—0
VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models—0
VITA: Versatile Time Representation Learning for Temporal Hyper-Relational Knowledge Graphs—0
Drug-Drug Interaction Prediction with Wasserstein Adversarial Autoencoder-based Knowledge Graph Embeddings—0
Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path—0
Weakly supervised collective feature learning from curated media—0
Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks—0
5* Knowledge Graph Embeddings with Projective Transformations—0
When Graph Neural Networks Meet Dynamic Mode Decomposition—0
Empirical effect of graph embeddings on fraud detection/ risk mitigation—0
A Survey of Link Prediction in Temporal Networks—0
Why Settle for Just One? Extending EL++ Ontology Embeddings with Many-to-Many Relationships—0
wsGAT: Weighted and Signed Graph Attention Networks for Link Prediction—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