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 1201–1225 of 1949 papers

TitleStatusHype
Dual Convolutional Neural Network for Graph of Graphs Link Prediction—0
Dual Graph Embedding for Object-Tag LinkPrediction on the Knowledge Graph—0
Stabilizing the Kumaraswamy Distribution—0
STaR: Knowledge Graph Embedding by Scaling, Translation and Rotation—0
STaR: Knowledge Graph Embedding by Scaling, Translation and Rotation—0
Argument Mining using BERT and Self-Attention based Embeddings—0
Statistical Guarantees for Link Prediction using Graph Neural Networks—0
Stochastic Blockmodels meet Graph Neural Networks—0
Stochastic Block Models with Multiple Continuous Attributes—0
Dual Node and Edge Fairness-Aware Graph Partition—0
DURENDAL: Graph deep learning framework for temporal heterogeneous networks—0
DVE: Dynamic Variational Embeddings with Applications in Recommender Systems—0
Adaptive Convolution for Multi-Relational Learning—0
Structural Explanations for Graph Neural Networks using HSIC—0
Structural Imbalance Aware Graph Augmentation Learning—0
Structure-Aware Random Fourier Kernel for Graphs—0
Structure Enhanced Graph Neural Networks for Link Prediction—0
DyExplainer: Explainable Dynamic Graph Neural Networks—0
Subgraph2vec: A random walk-based algorithm for embedding knowledge graphs—0
Subgraph-aware Few-Shot Inductive Link Prediction via Meta-Learning—0
Sub-graph Based Diffusion Model for Link Prediction—0
DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs—0
Symmetry-driven network reconstruction through pseudobalanced coloring optimization—0
Synthetic Graph Generation to Benchmark Graph Learning—0
Systematic Biases in Link Prediction: comparing heuristic and graph embedding based methods—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