SOTAVerified

Graph Embedding

Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.

( Image credit: GAT )

Papers

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Showing 521–530 of 1192 papers

TitleStatusHype
Inductively Representing Out-of-Knowledge-Graph Entities by Optimal Estimation Under Translational AssumptionsCode0
Hyperparameter-free and Explainable Whole Graph EmbeddingCode0
OCGEC: One-class Graph Embedding Classification for DNN Backdoor DetectionCode0
HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk PredictionCode0
Hierarchical Aggregations for High-Dimensional Multiplex Graph EmbeddingCode0
Hierarchical Latent Relation Modeling for Collaborative Metric LearningCode0
GREG: A Global Level Relation Extraction with Knowledge Graph Embedding—0
Grassmann Graph Embedding—0
Degree-Based Random Walk Approach for Graph Embedding—0
AutoETER: Automated Entity Type Representation for Knowledge Graph Embedding—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified