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 1151–1200 of 1949 papers

TitleStatusHype
Higher-Order Relations Skew Link Prediction in Graphs—0
The CAT SET on the MAT: Cross Attention for Set Matching in Bipartite HypergraphsCode0
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood PredictionCode0
Barlow Graph Auto-Encoder for Unsupervised Network Embedding—0
Deeper-GXX: Deepening Arbitrary GNNs—0
TME-BNA: Temporal Motif-Preserving Network Embedding with Bicomponent Neighbor AggregationCode0
Degree-Based Random Walk Approach for Graph Embedding—0
Drug Re-positioning via Text Augmented Knowledge Graph Embeddings—0
Why Settle for Just One? Extending EL++ Ontology Embeddings with Many-to-Many Relationships—0
Graph Partner Neural Networks for Semi-Supervised Learning on Graphs—0
Understanding the network formation pattern for better link predictionCode0
POLE: Polarized Embedding for Signed NetworksCode0
Prix-LM: Pretraining for Multilingual Knowledge Base ConstructionCode0
Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding—0
Residual2Vec: Debiasing graph embedding with random graphsCode0
Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction—0
Simplicial Convolutional Neural Networks—0
Inductive Representation Learning in Temporal Networks via Mining Neighborhood and Community InfluencesCode0
Latent Network Embedding via Adversarial Auto-encoders—0
How Neural Processes Improve Graph Link PredictionCode0
Revisiting Virtual Nodes in Graph Neural Networks for Link Prediction—0
Explaining Knowledge Graph Embedding via Latent Rule Learning—0
Explainable Automatic Hypothesis Generation via High-order Graph Walks—0
Online graph nets—0
A Deep Latent Space Model for Directed Graph Representation Learning—0
Scalable Hierarchical Embeddings of Complex Networks—0
Equivariant Heterogeneous Graph Networks—0
Few-shot graph link prediction with domain adaptation—0
MULTI-LEVEL APPROACH TO ACCURATE AND SCALABLE HYPERGRAPH EMBEDDING—0
End-to-End Learning of Probabilistic Hierarchies on Graphs—0
ConTIG: Continuous Representation Learning on Temporal Interaction Graphs—0
Updating Embeddings for Dynamic Knowledge Graphs—0
mGNN: Generalizing the Graph Neural Networks to the Multilayer Case—0
wsGAT: Weighted and Signed Graph Attention Networks for Link Prediction—0
Harnessing the Power of Ego Network Layers for Link Prediction in Online Social Networks—0
Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chains—0
HeMI: Multi-view Embedding in Heterogeneous GraphsCode0
r-GAT: Relational Graph Attention Network for Multi-Relational Graphs—0
Ergodic Limits, Relaxations, and Geometric Properties of Random Walk Node Embeddings—0
QUINT: Node embedding using network hashing—0
HMSG: Heterogeneous Graph Neural Network based on Metapath Subgraph Learning—0
Job Posting-Enriched Knowledge Graph for Skills-based Matching—0
Discussion Structure Prediction Based on a Two-step Method—0
Heterogeneous Graph Neural Network with Multi-view Representation Learning—0
Influence-guided Data Augmentation for Neural Tensor CompletionCode0
Integrating Transductive And Inductive Embeddings Improves Link Prediction Accuracy—0
Temporal Network Embedding via Tensor Factorization—0
Unsupervised Domain-adaptive Hash for Networks—0
Semi-supervised Network Embedding with Differentiable Deep Quantisation—0
Temporal Graph Network Embedding with Causal Anonymous Walks RepresentationsCode0
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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