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 1176–1192 of 1192 papers

TitleStatusHype
Cross-validation of matching correlation analysis by resampling matching weights—0
LINE: Large-scale Information Network EmbeddingCode1
A simple coding for cross-domain matching with dimension reduction via spectral graph embedding—0
Transition-based Knowledge Graph Embedding with Relational Mapping Properties—0
A Deep Graph Embedding Network Model for Face Recognition—0
Knowledge Graph Embedding by Translating on HyperplanesCode0
Estimating Vector Fields on Manifolds and the Embedding of Directed Graphs—0
Coarse-to-Fine Classification via Parametric and Nonparametric Models for Computer-Aided Diagnosis—0
Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds—0
Embedding Graphs under Centrality Constraints for Network Visualization—0
Dictionary Learning and Sparse Coding on Grassmann Manifolds: An Extrinsic Solution—0
Spectral Clustering for Divide-and-Conquer Graph MatchingCode0
Out-of-sample Extension for Latent Position Graphs—0
Multiple Kernel Sparse Representations for Supervised and Unsupervised Learning—0
Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators—0
Learning a Distance Metric from a Network—0
Dimensionality Reduction for Data in Multiple Feature Representations—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified