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

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Showing 1301–1325 of 1949 papers

TitleStatusHype
Progresses and Challenges in Link Prediction—0
Knowledge Hypergraph Embedding Meets Relational AlgebraCode0
Link Prediction Approach to Recommender Systems—0
Temporal-Amount Snapshot MultiGraph for Ethereum Transaction Tracking—0
A Hidden Challenge of Link Prediction: Which Pairs to Check?Code0
Adversarial Attack on Network Embeddings via Supervised Network PoisoningCode0
Knowledge Graph Embedding using Graph Convolutional Networks with Relation-Aware Attention—0
A Statistical Relational Approach to Learning Distance-based GCNs—0
Memory-Associated Differential LearningCode0
Understanding Higher-order Structures in Evolving Graphs: A Simplicial Complex based Kernel Estimation Approach—0
Hyperedge Prediction using Tensor Eigenvalue DecompositionCode0
Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs—0
Learning Graph Representations—0
LinkLouvain: Link-Aware A/B Testing and Its Application on Online Marketing Campaign—0
Heterogeneous Graph based Deep Learning for Biomedical Network Link Prediction—0
Calibrating and Improving Graph Contrastive LearningCode0
Knowledge Generation -- Variational Bayes on Knowledge GraphsCode0
NEMR: Network Embedding on Metric of Relation—0
GraphAttacker: A General Multi-Task GraphAttack FrameworkCode0
JITuNE: Just-In-Time Hyperparameter Tuning for Network Embedding Algorithms—0
BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network—0
Disentangling homophily, community structure and triadic closure in networks—0
A Survey on Embedding Dynamic Graphs—0
Representation Learning of Reconstructed Graphs Using Random Walk Graph Convolutional Network—0
Graph Edit NetworksCode0
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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