Abstract
In recent years, with the rapid growth of network scale, it becomes difficult to detect communities in large-scale networks for many existing algorithms. In this paper, a novel Parallel Multi-Label Propagation Algorithm (PMLPA) is proposed to detect the overlapping communities in networks. PMLPA employs a new label updating strategy using ankle-value in the label propagation procedure during each iteration. The new algorithm is implemented in the Spark framework for its power in distributed parallel computation. Experiments on artificial and real networks show that PMLPA is effective and efficient in community detection in large-scale networks.
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Acknowledgments
This work is partly supported by the National Natural Science Foundation of China under Grants No. 61103175 and No. 61300104, the Key Project of Chinese Ministry of Education under Grant No. 212086, the Fujian Province High School Science Fund for Distinguished Young Scholars under Grand No. JA12016, the Program for New Century Excellent Talents in Fujian Province University under Grant No. JA13021, the Fujian Natural Science Funds for Distinguished Young Scholar under Grant No. 2014J06017, and the Natural Science Foundation of Fujian Province under Grant No. 2013J01230.
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Li, R., Guo, W., Guo, K., Qiu, Q. (2015). Parallel Multi-label Propagation for Overlapping Community Detection in Large-Scale Networks. In: Bikakis, A., Zheng, X. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2015. Lecture Notes in Computer Science(), vol 9426. Springer, Cham. https://doi.org/10.1007/978-3-319-26181-2_33
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DOI: https://doi.org/10.1007/978-3-319-26181-2_33
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