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 1701–1725 of 1949 papers

TitleStatusHype
A Deep Sequential Model for Discourse Parsing on Multi-Party DialoguesCode0
Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs—0
Link Prediction in Networks with Core-Fringe DataCode0
Attributed Network Embedding for Incomplete Attributed NetworksCode0
Node Embedding with Adaptive Similarities for Scalable Learning over GraphsCode0
DynamicGEM: A Library for Dynamic Graph Embedding MethodsCode0
Adversarial Classifier for Imbalanced Problems—0
MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional NetworksCode0
Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation LearningCode0
Link Prediction in Dynamic Graphs for Recommendation—0
Learning Numerical Attributes in Knowledge Bases—0
Differentiating Concepts and Instances for Knowledge Graph EmbeddingCode0
End-to-end Structure-Aware Convolutional Networks for Knowledge Base CompletionCode0
A simple yet effective baseline for non-attributed graph classificationCode0
Multi-Task Graph AutoencodersCode0
Towards Sparse Hierarchical Graph ClassifiersCode0
ATP: Directed Graph Embedding with Asymmetric Transitivity PreservationCode0
Augmenting Compositional Models for Knowledge Base Completion Using Gradient Representations—0
MOHONE: Modeling Higher Order Network Effects in KnowledgeGraphs via Network Infused Embeddings—0
DOLORES: Deep Contextualized Knowledge Graph Embeddings—0
Data Poisoning Attack against Unsupervised Node Embedding Methods—0
Node Representation Learning for Directed Graphs—0
Binarized Attributed Network EmbeddingCode0
TNE: A Latent Model for Representation Learning on Networks—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