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 561–570 of 1192 papers

TitleStatusHype
Explaining Knowledge Graph Embedding via Latent Rule Learning—0
Why does Negative Sampling not Work Well? Analysis of Convexity in Negative Sampling—0
Scalable Hierarchical Embeddings of Complex Networks—0
Graph Similarities and Dual Approach for Sequential Text-to-Image Retrieval—0
GARNET: A Spectral Approach to Robust and Scalable Graph Neural Networks—0
MULTI-LEVEL APPROACH TO ACCURATE AND SCALABLE HYPERGRAPH EMBEDDING—0
Extracting Attentive Social Temporal Excitation for Sequential Recommendation—0
DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal GraphsCode0
One-Hot Graph Encoder EmbeddingCode1
DemiNet: Dependency-Aware Multi-Interest Network with Self-Supervised Graph Learning for Click-Through Rate Prediction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified