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 1801–1850 of 1949 papers

TitleStatusHype
Mandolin: A Knowledge Discovery Framework for the Web of DataCode0
Erratum: Link prediction in drug-target interactions network using similarity indices—0
Graph Attention NetworksCode1
Watch Your Step: Learning Node Embeddings via Graph AttentionCode0
Inductive Representation Learning in Large Attributed Graphs—0
Learning Social Image Embedding with Deep Multimodal Attention Networks—0
LASAGNE: Locality And Structure Aware Graph Node Embedding—0
SMR: Medical Knowledge Graph Embedding for Safe Medicine Recommendation—0
Community Aware Random Walk for Network Embedding—0
A Dynamic Edge Exchangeable Model for Sparse Temporal Networks—0
Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information NetworksCode0
On the Model Shrinkage Effect of Gamma Process Edge Partition Models—0
A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications—0
A statistical interpretation of spectral embedding: the generalised random dot product graph—0
On Multi-Relational Link Prediction with Bilinear Models—0
Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge GraphsCode0
Neural Factorization Machines for Sparse Predictive AnalyticsCode1
Weisfeiler-lehman neural machine for link predictionCode0
metapath2vec: Scalable Representation Learning for Heterogeneous NetworksCode0
Adversarial Sets for Regularising Neural Link PredictorsCode0
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via RankingCode0
Complex and Holographic Embeddings of Knowledge Graphs: A Comparison—0
Convolutional 2D Knowledge Graph EmbeddingsCode1
CANE: Context-Aware Network Embedding for Relation ModelingCode0
NPGLM: A Non-Parametric Method for Temporal Link Prediction—0
Deep Generative Models for Relational Data with Side Information—0
Leveraging Node Attributes for Incomplete Relational DataCode0
Attention Is All You NeedCode3
Graph Convolutional Matrix CompletionCode1
Inductive Representation Learning on Large GraphsCode1
Attributed Network Embedding for Learning in a Dynamic Environment—0
A generalized method toward drug-target interaction prediction via low-rank matrix projectionCode0
End-to-End Differentiable ProvingCode1
Consistencies and inconsistencies between model selection and link prediction in networks—0
Learning Edge Representations via Low-Rank Asymmetric ProjectionsCode0
Analogical Inference for Multi-Relational EmbeddingsCode0
Cross view link prediction by learning noise-resilient representation consensus—0
Improving Neural Knowledge Base Completion with Cross-Lingual Projections—0
Efficient Parallel Translating Embedding For Knowledge GraphsCode0
A survey of embedding models of entities and relationships for knowledge graph completionCode0
Modeling Relational Data with Graph Convolutional NetworksCode1
Classification in biological networks with hypergraphlet kernels—0
Knowledge Graph Completion via Complex Tensor FactorizationCode0
On the Equivalence of Holographic and Complex Embeddings for Link Prediction—0
Hierarchical Hyperlink Prediction for the WWW—0
Forward Backward Similarity Search in Knowledge Networks—0
Knowledge Graph Representation with Jointly Structural and Textual Encoding—0
Variational Graph Auto-EncodersCode1
The NOESIS Network-Oriented Exploration, Simulation, and Induction System—0
Link Prediction using Embedded Knowledge Graphs—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
8ComplEx-N3 (reciprocal)Hits@100.96—Unverified
9MEI (small)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