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 1751–1800 of 1949 papers

TitleStatusHype
AceKG: A Large-scale Knowledge Graph for Academic Data Mining—0
LinkNBed: Multi-Graph Representation Learning with Entity Linkage—0
Attention Models in Graphs: A SurveyCode0
Learning Deep Network Representations with Adversarially Regularized AutoencodersCode0
Knowledge Graph Embedding with Numeric Attributes of Entities—0
Towards Understanding the Geometry of Knowledge Graph EmbeddingsCode0
A Sequence Learning Method for Domain-Specific Entity Linking—0
Type-Sensitive Knowledge Base Inference Without Explicit Type SupervisionCode0
Jack the Reader -- A Machine Reading FrameworkCode0
Spectral Network Embedding: A Fast and Scalable Method via Sparsity—0
Scientific Discovery as Link Prediction in Influence and Citation Graphs—0
Accurate Text-Enhanced Knowledge Graph Representation Learning—0
Diffusion Maps for Textual Network Embedding—0
Conditional Network Embeddings—0
GANE: A Generative Adversarial Network Embedding—0
Learning Heterogeneous Knowledge Base Embeddings for Explainable RecommendationCode0
Billion-scale Network Embedding with Iterative Random ProjectionCode0
RECS: Robust Graph Embedding Using Connection Subgraphs—0
t-PINE: Tensor-based Predictable and Interpretable Node Embeddings—0
Fast and scalable learning of neuro-symbolic representations of biomedical knowledge—0
Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network EmbeddingCode0
Models for Capturing Temporal Smoothness in Evolving Networks for Learning Latent Representation of NodesCode0
Graphite: Iterative Generative Modeling of GraphsCode0
Locally Private Bayesian Inference for Count ModelsCode0
Expeditious Generation of Knowledge Graph EmbeddingsCode0
Optimal link prediction with matrix logistic regression—0
Variational Knowledge Graph Reasoning—0
Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction dataCode0
Deep Learning in Mobile and Wireless Networking: A Survey—0
Link prediction for egocentrically sampled networks—0
Representation Learning over Dynamic Graphs—0
Stochastic Block Models with Multiple Continuous Attributes—0
Tensorial and bipartite block models for link prediction in layered networks and temporal networks—0
AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks—0
NetGAN: Generating Graphs via Random WalksCode0
Evaluating Overfit and Underfit in Models of Network Community StructureCode0
Learning to Make Predictions on Graphs with AutoencodersCode0
Simplicial Closure and higher-order link predictionCode0
Neural Tensor Factorization—0
Weakly supervised collective feature learning from curated media—0
SimplE Embedding for Link Prediction in Knowledge GraphsCode0
M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search—0
A Generative Model for Dynamic Networks with Applications—0
Beyond Markov Logic: Efficient Mining of Prediction Rules in Large GraphsCode0
Incorporating Literals into Knowledge Graph EmbeddingsCode0
Modeling polypharmacy side effects with graph convolutional networksCode0
Knowledge Graph Embedding with Multiple Relation Projections—0
Fast Node Embeddings: Learning Ego-Centric Representations—0
GraphGAN: Generating Graphs via Random Walks—0
Revisiting Knowledge Base Embedding as Tensor Decomposition—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