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 921–930 of 1192 papers

TitleStatusHype
Learning event representations for temporal segmentation of image sequences by dynamic graph embedding—0
Representation Learning of EHR Data via Graph-Based Medical Entity Embedding—0
GraphZoom: A multi-level spectral approach for accurate and scalable graph embeddingCode0
Dynamic Joint Variational Graph Autoencoders—0
Graph Analysis and Graph Pooling in the Spatial Domain—0
Deep Clustering by Gaussian Mixture Variational Autoencoders With Graph EmbeddingCode0
AttPool: Towards Hierarchical Feature Representation in Graph Convolutional Networks via Attention MechanismCode0
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings—0
Universal Graph Transformer Self-Attention NetworksCode0
A Group-Theoretic Framework for Knowledge Graph Embedding—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified