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 621–630 of 1192 papers

TitleStatusHype
Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Survey—0
Embedding Knowledge Graphs Attentive to Positional and Centrality QualitiesCode0
Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks—0
Network embedding unveils the hidden interactions in the mammalian virome—0
High Tension Lines: Predicting robustness of high-voltage power-grids to cascading failure using network embedding—0
Learning to Optimize Industry-Scale Dynamic Pickup and Delivery Problems—0
Out-of-Vocabulary Entities in Link PredictionCode0
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge—0
A Comprehensive Survey on Community Detection with Deep Learning—0
A unified framework based on graph consensus term for multi-view learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepGGEntropy Difference0—Unverified