Abstract
With the arrival of the era of big data, the scale of network has grown at an incredible rate, which has brought challenges to community discovery. Local community discovery is a kind of community discovery that does not need to know global information about network. A quantity of local community discovery algorithms have been put forward by researchers. Traditional local community discovery generally needs to define local community modularity Q, and greedily add nodes to the community when ΔQ > 0, which is easy to fall into the local optimal solution. Inspired by the ideal of simulated annealing, greedy algorithm with probability LCDGAP is proposed to detect local community in this paper, which can be applied to all algorithms that perform greedy addition. We permit that the node can be aggregated into the community with a certain probability when ΔQ < 0. At the same time, we guarantee that this probability will be getting smaller with the increase of program running time, ensuring the program’s convergence and stability. Experimental result proves that LCDGAP performs effectively not only in real-world dataset but also computer-generated dataset.
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References
Chen, W., Wang, S., Zhang, X., et al.: EEG-based motion intention recognition via multi-task RNNs. In: pp. 279–287. Society for Industrial and Applied Mathematics (2018)
Yue, L., Chen, W., Li, X., et al.: A survey of sentiment analysis in social media. Knowl. Inf. Syst. 1–47 (2018)
Xia, Z., Bu, Z.: Community detection based on a semantic network. Knowl.-Based Syst. 26, 30–39 (2012)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)
Bu, Z., Zhang, C., Xia, Z.: A fast parallel modularity optimization algorithm for community detection in online social network. Knowl.-Based Syst. 176(3) (2013)
Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. 2008(10) (2008)
Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76(3), 036106 (2006)
Zhang, X.-K., Fei, S., Song, C., et al.: Label propagation algorithm based on local cycles for community detection. Int. J. Mod. Phys. B 29(05), 1550029 (2015)
Clauset, A.: Finding local community structure in networks. Phys. Rev. E 72(2), 026132 (2005)
Luo, F., Wang, J.Z., Promislow, E.: Exploring local community structures in large networks. Web Intell. Agent Syst. 6(4), 387–400 (2008)
Chen, J., Zaïane, O., Goebel, R.: Local community identification in social networks. In: Advances in Social Network Analysis and Mining, pp. 237–242 (2009)
Lancichinetti, A., Fortunato, S., Kertész, J.: Detecting the overlapping and hierarchical community structure of complex networks. New J. Phys. 11(3), 19–44 (2009)
Xia, S., Zhou, R., Zhou, Y., Zhu, M.: An improved local community detection algorithm using selection probability. Math. Prob. Eng. 2014(2), 1–10 (2014)
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Zhu, X., Xia, Z. (2018). Local Community Detection Using Greedy Algorithm with Probability. In: Gan, G., Li, B., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2018. Lecture Notes in Computer Science(), vol 11323. Springer, Cham. https://doi.org/10.1007/978-3-030-05090-0_40
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DOI: https://doi.org/10.1007/978-3-030-05090-0_40
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