dyngraph2vec: Capturing network dynamics using dynamic graph representation learning

Learning graph representations is a fundamental task aimed at capturing various properties of graphs in vector space. The most recent methods learn such representations for static networks. However, real-world networks evolve over time and have varying dynamics. Capturing such evolution is key to pr...

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Veröffentlicht in:	Knowledge-based systems Jg. 187; S. 104816
Hauptverfasser:	Goyal, Palash, Chhetri, Sujit Rokka, Canedo, Arquimedes
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.01.2020 Elsevier Science Ltd
Schlagworte:	Datasets Dynamics Efficacy Embedding Evolution Graph embedding applications Graph embedding techniques Graph representations Graphical representations Graphs Learning Machine learning Networks Performance prediction Predictions Property Python graph embedding methods GEM library Recurrent Stochastic models Python graph embedding methods GEM library Graph embedding applications Graph embedding techniques
ISSN:	0950-7051, 1872-7409
Online-Zugang:	Volltext
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