Understanding Graph Embedding Methods and Their Applications
Mengjia Xu
Abstract
Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 20 December 2020Accepted: 23 February 2021Published online: 04 November 2021Keywordsdeep neural networks, high-dimensionality, latent space, similarity, uncertainty quantification, intrinsic dimension, graph embedding at scaleAMS Subject Headings68T07, 05C62, 94A15, 68T37, 68R10, 68T30Publication DataISSN (print): 0036-1445ISSN (online): 1095-7200Publisher: Society for Industrial and Applied MathematicsCODEN: siread
Topics & Concepts
EmbeddingCurse of dimensionalityGraphComputer scienceDimensionality reductionGraph embeddingSimilarity (geometry)Dimension (graph theory)Artificial intelligenceMachine learningSubject (documents)Theoretical computer scienceInformation retrievalMathematicsCombinatoricsWorld Wide WebImage (mathematics)Advanced Graph Neural NetworksMachine Learning in Materials ScienceComplex Network Analysis Techniques