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 1551–1600 of 1949 papers

TitleStatusHype
Deep Hashing for Signed Social Network Embedding—0
Reconstructing commuters network using machine learning and urban indicators—0
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation LearningCode0
Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings—0
Using hyperbolic large-margin classifiers for biological link predictionCode0
Probabilistic Models of Relational ImplicationCode0
DynWalks: Global Topology and Recent Changes Awareness Dynamic Network EmbeddingCode1
Node Attribute Generation on GraphsCode1
Hyperlink Regression via Bregman Divergence—0
Mining Temporal Evolution of Knowledge Graph and Genealogical Features for Literature-based Discovery PredictionCode0
Differentially Private Link Prediction With Protected Connections—0
Snomed2Vec: Random Walk and Poincaré Embeddings of a Clinical Knowledge Base for Healthcare AnalyticsCode0
DeepNC: Deep Generative Network CompletionCode0
DeepTrax: Embedding Graphs of Financial Transactions—0
Towards Probabilistic Generative Models Harnessing Graph Neural Networks for Disease-Gene Prediction—0
Adaptive Margin Ranking Loss for Knowledge Graph Embeddings via a Correntropy Objective Function—0
Graph Embeddings at Scale—0
Duality of Link Prediction and Entailment Graph InductionCode0
A2N: Attending to Neighbors for Knowledge Graph Inference—0
Unsupervised Adversarial Graph Alignment with Graph Embedding—0
Augmenting and Tuning Knowledge Graph EmbeddingsCode0
Signed Graph Attention NetworksCode1
Dynamic Network Embeddings for Network Evolution Analysis—0
Predicting kills in Game of Thrones using network propertiesCode0
Graph Star Net for Generalized Multi-Task LearningCode0
ANAE: Learning Node Context Representation for Attributed Network Embedding—0
An Open-World Extension to Knowledge Graph Completion ModelsCode0
Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank—0
Learning Correlated Latent Representations with Adaptive Priors—0
Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node DescriptorsCode0
Neural Variational Inference For Estimating Uncertainty in Knowledge Graph EmbeddingsCode0
Graph Embedding on Biomedical Networks: Methods, Applications, and EvaluationsCode0
Higher-Order Ranking and Link Prediction: From Closing Triangles to Closing Higher-Order Motifs—0
Position-aware Graph Neural NetworksCode0
Dynamic Network Embedding via Incremental Skip-gram with Negative SamplingCode0
Learning Attention-based Embeddings for Relation Prediction in Knowledge GraphsCode0
Relation Embedding with Dihedral Group in Knowledge Graph—0
Abstract Graphs and Abstract Paths for Knowledge Graph Completion—0
Adaptive Convolution for Multi-Relational Learning—0
Knowledge Hypergraphs: Prediction Beyond Binary RelationsCode0
End to end learning and optimization on graphsCode0
Graph Learning Network: A Structure Learning AlgorithmCode0
STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender SystemsCode0
FOBE and HOBE: First- and High-Order Bipartite Embeddings—0
MDE: Multiple Distance Embeddings for Link Prediction in Knowledge GraphsCode0
Is a Single Vector Enough? Exploring Node Polysemy for Network EmbeddingCode0
Spring-Electrical Models For Link PredictionCode0
GLEE: Geometric Laplacian Eigenmap EmbeddingCode0
Knowledge Graph Embedding Bi-Vector Models for Symmetric Relation—0
Gravity-Inspired Graph Autoencoders for Directed Link PredictionCode0
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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