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

Showing 14261450 of 1949 papers

TitleStatusHype
Open the Eyes of MPNN: Vision Enhances MPNN in Link Prediction0
Adversarial Network Embedding0
OpticE: A Coherence Theory-Based Model for Link Prediction0
Using Adamic-Adar Index Algorithm to Predict Volunteer Collaboration: Less is More0
Optimal link prediction with matrix logistic regression0
Optimizing Long-tailed Link Prediction in Graph Neural Networks through Structure Representation Enhancement0
Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding0
TNE: A Latent Model for Representation Learning on Networks0
Over-Squashing in Graph Neural Networks: A Comprehensive survey0
Bridging Source and Target Domains via Link Prediction for Unsupervised Domain Adaptation on Graphs0
Adversarial Learning for Debiasing Knowledge Graph Embeddings0
A Statistical Relational Approach to Learning Distance-based GCNs0
Pair-view Unsupervised Graph Representation Learning0
Brain-inspired sparse training enables Transformers and LLMs to perform as fully connected0
Birds of a Feather Linked Together: A Discriminative Topic Model using Link-based Priors0
Bipartite Link Prediction based on Topological Features via 2-hop Path0
PanRep: Universal node embeddings for heterogeneous graphs0
Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis0
Biomedical Network Link Prediction using Neural Network Graph Embedding0
Parameter-Efficient Tuning Large Language Models for Graph Representation Learning0
Biomedical Knowledge Graph Refinement and Completion using Graph Representation Learning and Top-K Similarity Measure0
What Do LLMs Need to Understand Graphs: A Survey of Parametric Representation of Graphs0
PatentMiner: Patent Vacancy Mining via Context-enhanced and Knowledge-guided Graph Attention0
Topic-aware latent models for representation learning on networks0
Topic Modeling and Link-Prediction for Material Property Discovery0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AutoKGEHits@100.56Unverified
2CP-N3-RPHits@100.55Unverified
3DistMult (after variational EM)Hits@100.55Unverified
4KG-R3Hits@100.54Unverified
5LASSHits@100.53Unverified
6MDE_advHits@100.53Unverified
7GFA-NNHits@100.52Unverified
8KGRefinerHits@100.49Unverified
9ComplEx NSCachingHits@100.48Unverified
10LogicENNHits@100.47Unverified
#ModelMetricClaimedVerifiedStatus
1MoCoKGCHits@100.88Unverified
2KERMITHits@100.83Unverified
3MoCoSAHits@100.82Unverified
4SimKGCIB(+PB+SN)Hits@100.82Unverified
5C-LMKE(bert-base)Hits@100.79Unverified
6LASSHits@100.79Unverified
7LP-BERTHits@100.75Unverified
8KGLMHits@100.74Unverified
9StAR(Self-Adp)Hits@100.71Unverified
10PALTHits@100.69Unverified
#ModelMetricClaimedVerifiedStatus
1OpenKE (han2018openke)training time (s)11Unverified
2LibKGE (ruffinelli2020you)training time (s)10Unverified
3GraphVite (zhu2019graphvite)training time (s)6Unverified
4Inverse ModelHits@100.96Unverified
5QuatDEHits@100.96Unverified
6LineaREHits@100.96Unverified
7AutoKGEHits@100.96Unverified
8MEI (small)Hits@100.96Unverified
9ComplEx-N3 (reciprocal)Hits@100.96Unverified
10RotatEHits@100.96Unverified
#ModelMetricClaimedVerifiedStatus
1OPTransEHits@100.9Unverified
2AutoKGEMRR0.86Unverified
3ComplEx-N3 (reciprocal)MRR0.86Unverified
4LineaREMRR0.84Unverified
5DistMult (after variational EM)MRR0.84Unverified
6QuatEMRR0.83Unverified
7SEEKMRR0.83Unverified
8MEI-BTDMRR0.81Unverified
9MEI (small)MRR0.8Unverified
10pRotatEMRR0.8Unverified