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Efficient Community Detection Based on Label Propagation with Belonging Coefficient and Edge Probability

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Social Media Processing (SMP 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 669))

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Abstract

Label propagation algorithm (LPA) has proven to be an efficient means for finding communities in large complex networks, and many improved methods were proposed, but the performance, stability and time complexity of them still need to be improved. In this paper, we investigate the modularity-specialized label propagation algorithm (LPAm), and find that the time complexity of LPAm greatly increased. We prune the LPAm algorithm by only considering neighbors for updating a node’s label, which degenerates to a Label propagation algorithm with edge probability and retains the same computational efficiency with LPA. Further, we integrate maximum belonging coefficient into LPA and present an advanced label propagation algorithm by combining maximum belonging coefficient and edge probability (LPAbp), which improves the quality of communities and preserves the merit of high speed of LPA. We also discuss the formation of monster community and time complexity of LPA, LPAm, and our algorithm by experiments on real world networks in form of quantitative analysis. Our proposed algorithms were evaluated on fourteen networks of various types and sizes. Experiments show that the LPAbp algorithm sustains the same time complexity with LPA, hinders the formation of monster community, and exhibits significant improvements in the modularity and Normalized mutual information values of community detection.

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Acknowledgments

This research is supported by Grant DP140103157 from the Australian Research Council (ARC Discovery Project), the National Natural Science Foundation of China (No. 61402119), the Humanities and Social Sciences Research Youth Foundation of Ministry of Education of China (No. 13YJCZH258), The Training Program for Outstanding Young Teachers in University of Guangdong Province (No. GWTPSY201403), The United Youth Fund Project of Guangdong University of Foreign Studies (No. 12s10).

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Authors and Affiliations

  1. Cisco School of Informatics, Guangdong University of Foreign Studies, Guangzhou, 510006, Guangdong, China

    Xinmeng Zhang, Shengyi Jiang, Bailin Xie & Meixiu Lu

  2. Social Science Key Laboratory of Language Engineering and Computing of Guangdong Province, Guangdong University of Foreign Studies, Guangzhou, 510006, Guangdong, China

    Xinmeng Zhang, Shengyi Jiang, Bailin Xie, Xia Li & Qiansheng Zhang

  3. School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, QLD, 4001, Australia

    Yuefeng Li

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  1. Xinmeng Zhang
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Correspondence to Xinmeng Zhang .

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Editors and Affiliations

  1. Beijing Language and Culture University, Beijing, China

    Yuming Li

  2. Jiangxi Normal University, Nanchang, China

    Guoxiong Xiang

  3. Dalian University of Technology, Dalian, China

    Hongfei Lin

  4. Jiangxi Normal University, Nanchang, China

    Mingwen Wang

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Zhang, X. et al. (2016). Efficient Community Detection Based on Label Propagation with Belonging Coefficient and Edge Probability. In: Li, Y., Xiang, G., Lin, H., Wang, M. (eds) Social Media Processing. SMP 2016. Communications in Computer and Information Science, vol 669. Springer, Singapore. https://doi.org/10.1007/978-981-10-2993-6_5

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  • DOI: https://doi.org/10.1007/978-981-10-2993-6_5

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