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Network community detection based on the physarum-inspired computational framework

journal contribution
posted on 2018-11-01, 00:00 authored by C Gao, M Liang, X Li, Zili ZhangZili Zhang, Z Wang, Z Zhou
IEEE 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 been proposed for solving such a problem. Due to the inherent complexity of identifying network structure, how to design an effective algorithm with a higher accuracy and a lower computational cost still remains an open problem. Inspired by the computational capability and positive feedback mechanism in the wake of foraging process of Physarum, which is a large amoeba-like cell consisting of a dendritic network of tube-like pseudopodia, a general Physarum-based computational framework for community detection is proposed in this paper. Based on the proposed framework, the inter-community edges can be identified from the intra-community edges in a network and the positive feedback of solving process in an algorithm can be further enhanced, which are used to improve the efficiency of original optimization-based and heuristic-based community detection algorithms, respectively. Some typical algorithms (e.g., genetic algorithm, ant colony optimization algorithm, and Markov clustering algorithm) and real-world datasets have been used to estimate the efficiency of our proposed computational framework. Experiments show that the algorithms optimized by Physarum-inspired computational framework perform better than the original ones, in terms of accuracy and computational cost. Moreover, a computational complexity analysis verifies the scalability of our framework.

History

Journal

IEEE/ACM transactions on computational biology and bioinformatics

Volume

15

Season

Nov-Dec

Pagination

1916-1928

Location

Piscataway, N.J.

ISSN

1545-5963

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2016, IEEE

Issue

6

Publisher

IEEE

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