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 1301–1325 of 1949 papers

TitleStatusHype
TSAM: Temporal Link Prediction in Directed Networks based on Self-Attention Mechanism—0
A comparative study of similarity-based and GNN-based link prediction approaches—0
GraphReach: Position-Aware Graph Neural Network using Reachability EstimationsCode0
NASE: Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture SearchCode0
Relational Reflection Entity AlignmentCode1
TempNodeEmb:Temporal Node Embedding considering temporal edge influence matrixCode0
DensE: An Enhanced Non-commutative Representation for Knowledge Graph Embedding with Adaptive Semantic HierarchyCode0
DINE: A Framework for Deep Incomplete Network Embedding—0
Random Walks: A Review of Algorithms and Applications—0
Big Networks: A Survey—0
MODEL: Motif-based Deep Feature Learning for Link Prediction—0
Convolutional Complex Knowledge Graph EmbeddingsCode1
Learning-based link prediction analysis for Facebook100 network—0
Representation Learning of Graphs Using Graph Convolutional Multilayer Networks Based on Motifs—0
PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph EmbeddingsCode2
Learning Attribute-Structure Co-Evolutions in Dynamic GraphsCode0
On a Bernoulli Autoregression Framework for Link Discovery and Prediction—0
Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous NetworksCode1
The multilayer random dot product graphCode0
PanRep: Graph neural networks for extracting universal node embeddings in heterogeneous graphsCode1
Second-Order Pooling for Graph Neural NetworksCode1
Few-shot link prediction via graph neural networks for Covid-19 drug-repurposingCode0
A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning—0
Dual Graph Embedding for Object-Tag LinkPrediction on the Knowledge Graph—0
Inductive Link Prediction for Nodes Having Only Attribute InformationCode1
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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