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 681–690 of 1192 papers

TitleStatusHype
GARNET: A Spectral Approach to Robust and Scalable Graph Neural Networks—0
Graph Similarities and Dual Approach for Sequential Text-to-Image Retrieval—0
Scalable Hierarchical Embeddings of Complex Networks—0
Explaining Knowledge Graph Embedding via Latent Rule Learning—0
Convolutional Networks on Enhanced Message-Passing Graph Improve Semi-Supervised Classification with Few Labels—0
MULTI-LEVEL APPROACH TO ACCURATE AND SCALABLE HYPERGRAPH EMBEDDING—0
DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal GraphsCode0
Extracting Attentive Social Temporal Excitation for Sequential Recommendation—0
DemiNet: Dependency-Aware Multi-Interest Network with Self-Supervised Graph Learning for Click-Through Rate Prediction—0
Recommender systems based on graph embedding techniques: A comprehensive reviewCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified