GrAMME: Semisupervised Learning Using Multilayered Graph Attention Models

Volume: 31, Issue: 10, Pages: 3977 - 3988
Published: Oct 1, 2020
Abstract
Modern data analysis pipelines are becoming increasingly complex due to the presence of multiview information sources. While graphs are effective in modeling complex relationships, in many scenarios, a single graph is rarely sufficient to succinctly represent all interactions, and hence, multilayered graphs have become popular. Though this leads to richer representations, extending solutions from the single-graph case is not straightforward....
Paper Details
Title
GrAMME: Semisupervised Learning Using Multilayered Graph Attention Models
Published Date
Oct 1, 2020
Volume
31
Issue
10
Pages
3977 - 3988
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