Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art

Graphs have been widely used to represent complex data in many applications, such as e-commerce, social networks, and bioinformatics. Efficient and effective analysis of graph data is important for graph-based applications. However, most graph analysis tasks are combinatorial optimization (CO) probl...

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Veröffentlicht in:	Data Science and Engineering Jg. 6; H. 2; S. 119 - 141
Hauptverfasser:	Peng, Yun, Choi, Byron, Xu, Jianliang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Singapore Springer Singapore 01.06.2021 Springer Springer Nature B.V
Schlagworte:	Algorithm Analysis and Problem Complexity Artificial Intelligence Bioinformatics Carbon monoxide Chemistry and Earth Sciences Combinatorial analysis Computer Science Data Mining and Knowledge Discovery Database Management Embedding Graph representations Graphical representations Graphs Machine learning Optimization Physics Social networks Statistics for Engineering Surveys Systems and Data Security Graph representation learning Combinational optimization Graph neural network
ISSN:	2364-1185, 2364-1541
Online-Zugang:	Volltext
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