Abstract
Community mining is a powerful tool for discovering the knowledge of networks and has a wide application. The modularity is one of very popular measurements for evaluating the efficiency of community divisions. However, the modularity maximization is a NP-complete problem. As an effective optimization algorithm for solving NP-complete problems, ant colony based community detection algorithm has been proposed to deal with such task. However the low accuracy and premature still limit its performance. Aiming to overcome those shortcomings, this paper proposes a novel nature-inspired optimization for the community mining based on the Physarum, a kind of slime molds cells. In the proposed strategy, the Physarum-inspired model optimizes the heuristic factor of ant colony algorithm by endowing edges with weights. With the information of weights provided by the Physarum-inspired model, the optimized heuristic factor can improve the searching abilities of ant colony algorithms. Four real-world networks and two typical kinds of ant colony optimization algorithms are used for estimating the efficiency of proposed strategy. Experiments show that the optimized ant colony optimization algorithms can achieve a better performance in terms of robustness and accuracy with a lower computational cost.
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Acknowledgement
Prof. Zili Zhang and Dr. Xianghua Li are the corresponding authors. This work is supported by the National Natural Science Foundation of China (Nos. 61403315,61402379), CQ CSTC (No. cstc2015gjhz40002), Fundamental Research Funds for the Central Universities (Nos. XDJK2016A008, XDJK2016B029, XDJK2016D053) and Chongqing Graduate Student Research Innovation Project (No. CYS16067).
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Liang, M., Gao, C., Li, X., Zhang, Z. (2017). A Physarum-Inspired Ant Colony Optimization for Community Mining. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10234. Springer, Cham. https://doi.org/10.1007/978-3-319-57454-7_57
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DOI: https://doi.org/10.1007/978-3-319-57454-7_57
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