Abstract
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.
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Acknowledgments
This work was supported by National Natural Science Foundation of China (Nos. 61402379, 61403315), Natural Science Foundation of Chongqing (No. cstc20 13jcyjA40022), Fundamental Research Funds for the Central Universities (Nos. XDJK2016D053, XDJK2016A008), Chongqing Graduate Student Research Innovation Project, and Specialized Research Fund for the Doctoral Program of Higher Education (No. 20120182120016). Prof. Zili Zhang and Dr. Xianghua Li are the corresponding authors of this paper.
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Liang, M., Li, X., Zhang, Z. (2016). A Physarum-Based General Computational Framework for Community Mining. In: Tan, Y., Shi, Y., Li, L. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9713. Springer, Cham. https://doi.org/10.1007/978-3-319-41009-8_15
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DOI: https://doi.org/10.1007/978-3-319-41009-8_15
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