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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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics Vol. 15; no. 6; pp. 1916 - 1928
Main Authors: Gao, Chao, Liang, Mingxin, Li, Xianghua, Zhang, Zili, Wang, Zhen, Zhou, Zhili
Format: Journal Article
Language:English
Published: United States IEEE 01.11.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
<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 Access:Get full text
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Summary: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 , a kind of slime, 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.
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ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2016.2638824