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 1676–1700 of 1949 papers

TitleStatusHype
Empirical effect of graph embeddings on fraud detection/ risk mitigation—0
Generative Temporal Link Prediction via Self-tokenized Sequence Modeling—0
r-GAT: Relational Graph Attention Network for Multi-Relational Graphs—0
GENIE: Watermarking Graph Neural Networks for Link Prediction—0
GENN: Predicting Correlated Drug-drug Interactions with Graph Energy Neural Networks—0
A Transfer Learning Framework for Multilayer Networks via Model Averaging—0
Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path—0
Geometric Algebra based Embeddings for Static and Temporal Knowledge Graph Completion—0
Geometric Feature Enhanced Knowledge Graph Embedding and Spatial Reasoning—0
VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models—0
RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning—0
Embedding Cardinality Constraints in Neural Link Predictors—0
GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint—0
GIDN: A Lightweight Graph Inception Diffusion Network for High-efficient Link Prediction—0
Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs—0
Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chains—0
NodeSig: Binary Node Embeddings via Random Walk Diffusion—0
Global and local evaluation of link prediction tasks with neural embeddings—0
RNNCTPs: A Neural Symbolic Reasoning Method Using Dynamic Knowledge Partitioning Technology—0
GNN-ACLP: Graph Neural Networks based Analog Circuit Link Prediction—0
GNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks—0
GNN Applied to Ego-nets for Friend Suggestions—0
Efficient Relational Context Perception for Knowledge Graph Completion—0
Robust Sequence Networked Submodular Maximization—0
GRADE: Graph Dynamic Embedding—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