Network Community Detection Based on the Physarum-Inspired Computational Framework

Community detection is a crucial and essential problem in the structure analytics of complex networks, which can help us understand and predict the characteristics and functions of complex networks. Many methods, ranging from the optimization-based algorithms to the heuristic-based algorithms, have...

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Veröffentlicht in:	IEEE/ACM transactions on computational biology and bioinformatics Jg. 15; H. 6; S. 1916 - 1928
Hauptverfasser:	Gao, Chao, Liang, Mingxin, Li, Xianghua, Zhang, Zili, Wang, Zhen, Zhou, Zhili
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.11.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Physarum Algorithm design and analysis Algorithms Ant colony optimization Clustering Clustering algorithms Communities Community detection Complexity Computational efficiency Computer applications Computing time Feedback genetic algorithm Genetic algorithms Heuristic algorithms Heuristic methods Linear programming Markov clustering algorithm Markov processes Physarum Positive feedback Slime
ISSN:	1545-5963, 1557-9964
Online-Zugang:	Volltext
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