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 941–950 of 1192 papers

TitleStatusHype
Dynamic Joint Variational Graph Autoencoders—0
Graph Analysis and Graph Pooling in the Spatial Domain—0
AttPool: Towards Hierarchical Feature Representation in Graph Convolutional Networks via Attention MechanismCode0
Deep Clustering by Gaussian Mixture Variational Autoencoders With Graph EmbeddingCode0
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings—0
Universal Graph Transformer Self-Attention NetworksCode0
Iterative Deep Graph Learning for Graph Neural Networks—0
Global graph curvature—0
A Group-Theoretic Framework for Knowledge Graph Embedding—0
Graph Neural Networks For Multi-Image Matching—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified