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A physarum-based general computational framework for community mining

Liang, Mingxin, Li, Xianghua and Zhang, Zili 2016, A physarum-based general computational framework for community mining. In Tan, Ying, Shi, Yuhui and Li, Li (ed), Advances in swarm intelligence, Springer, Cham, Switzerland, pp.141-149, doi: 10.1007/978-3-319-41009-8_15.

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Title A physarum-based general computational framework for community mining
Author(s) Liang, Mingxin
Li, Xianghua
Zhang, Zili
Title of book Advances in swarm intelligence
Editor(s) Tan, Ying
Shi, Yuhui
Li, Li
Publication date 2016
Series Lecture notes in computer science
Start page 141
End page 149
Total pages 9
Publisher Springer
Place of Publication Cham, Switzerland
Keyword(s) community mining
general computational framework
Physarum network mathematical model
Summary Community mining is a crucial and essential problem in complex networks analysis. Many algorithms have been proposed for solving such problem. However, the weaker robustness and lower accuracy still limit their efficiency. Aiming to overcome those shortcomings, this paper proposes a general Physarum-based computational framework for community mining. The proposed framework takes advantages of a unique characteristic of a Physarum-inspired network mathematical model, which can differentiate inter-community edges from intra-community edges in different type of networks and improve the efficiency of original detection algorithms. Some typical algorithms (e.g., genetic algorithm, ant colony optimization algorithm, and Markov clustering algorithm) and six real-world datasets have been used to estimate the efficiency of our proposed computational framework. Experiments show that the algorithms optimized by Physarum-inspired network mathematical model perform better than the original ones for community mining, in terms of robustness and accuracy. Moreover, a computational complexity analysis verifies the scalability of proposed framework.
ISBN 9783319410098
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-41009-8_15
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2016, Springer International Publishing Switzerland
Persistent URL http://hdl.handle.net/10536/DRO/DU:30091439

Document type: Book Chapter
Collection: School of Information Technology
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