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 951–1000 of 1949 papers

TitleStatusHype
POLE: Polarized Embedding for Signed NetworksCode0
Understanding the network formation pattern for better link predictionCode0
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
GRAPE for Fast and Scalable Graph Processing and random walk-based EmbeddingCode1
Neural Link Prediction with Walk PoolingCode1
Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction—0
Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph RepresentationsCode1
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
Few-shot graph link prediction with domain adaptation—0
Revisiting Virtual Nodes in Graph Neural Networks for Link Prediction—0
Benchmarking Graph Neural Networks on Dynamic Link PredictionCode1
Explaining Knowledge Graph Embedding via Latent Rule Learning—0
Scalable Hierarchical Embeddings of Complex Networks—0
End-to-End Learning of Probabilistic Hierarchies on Graphs—0
Online graph nets—0
Equivariant Heterogeneous Graph Networks—0
A Deep Latent Space Model for Directed Graph Representation Learning—0
MULTI-LEVEL APPROACH TO ACCURATE AND SCALABLE HYPERGRAPH EMBEDDING—0
Explainable Automatic Hypothesis Generation via High-order Graph Walks—0
ConTIG: Continuous Representation Learning on Temporal Interaction Graphs—0
How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence ViewCode1
Updating Embeddings for Dynamic Knowledge Graphs—0
wsGAT: Weighted and Signed Graph Attention Networks for Link Prediction—0
mGNN: Generalizing the Graph Neural Networks to the Multilayer Case—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
SAFRAN: An interpretable, rule-based link prediction method outperforming embedding modelsCode1
HeMI: Multi-view Embedding in Heterogeneous GraphsCode0
Graph Algorithms for Multiparallel Word AlignmentCode1
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
TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph ForecastingCode1
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
Knowledge Perceived Multi-modal Pretraining in E-commerceCode1
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
Variational Graph Normalized Auto-EncodersCode1
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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