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Application of Overlapping Community Discovery Algorithm in Complex Network Big Data

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Abstract

This paper first introduced the complex network and community discovery and then designed an improved community overlap propagation algorithm (COPRA) algorithm based on the labeling algorithm, i.e., improving the performance of the algorithm by reducing the initial labels and updating asynchronously. The results demonstrated that the algorithm designed in this study could also find overlapping communities when the community structure was not obvious, and its EQ value was always larger than other algorithms. The results of real data sets showed that the EQ value of ICOPRA was 62.86, 217, and 67.65% larger than SLPA, CPM, and COPRA, respectively, when Zachary was taken as an example, but the calculation time slightly increased. The experimental results show the effectiveness of the proposed method, which can be further promoted and applied in practice.

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REFERENCES

  1. Zhao, T., Jia, L., Dong, H., Su, F., and Zhang, Z., Analysis of urban road traffic network based on complex network, Procedia Eng., 2016, vol. 137, pp. 537–546.  https://doi.org/10.1016/j.proeng.2016.01.290

    Article  Google Scholar 

  2. Mall, R., Cerulo, L., Bensmail, H., Iavarone, A., and Ceccarelli, M., Detection of statistically significant network changes in complex biological networks, BMC Syst. Biol., 2017, vol. 11, p. 32.  https://doi.org/10.1186/s12918-017-0412-6

    Article  Google Scholar 

  3. Benyoussef, M., Ez-Zahraouy, H., and Benyoussef, A., Optimal topology to minimizing congestion in connected communication complex network, Int. J. Mod. Phys. C, 2017, vol. 28, no. 6, p. 1750073.  https://doi.org/10.1142/S0129183117500735

    Article  Google Scholar 

  4. Duan, L. and Binbasioglu, M., An ensemble framework for community detection, J. Ind. Inf. Integr., 2017, vol. 5, pp. 1–5.  https://doi.org/10.1016/j.jii.2017.01.001

    Article  Google Scholar 

  5. Ding, Z., Zhang, X., Sun, D., and Bin, L., Overlapping community detection based on network decomposition, Sci. Rep., 2016, vol. 6, p. 24115.  https://doi.org/10.1038/srep24115

    Article  Google Scholar 

  6. Wen, X., Chen, W.N., Lin, Y., Gu, T., Zhang, H., Li, Y., Yin, Y., and Zhang, J., A maximal clique based multiobjective evolutionary algorithm for overlapping community detection, IEEE Trans. Evol. Comput., 2017, vol. 21, no. 3, pp. 363–377.  https://doi.org/10.1109/TEVC.2016.2605501

    Article  Google Scholar 

  7. Bhat, S.Y. and Abulaish, M., OCMiner: A density-based overlapping community detection method for social networks, Intell. Data Anal., 2015, vol. 19, no. 4, pp. 917–947.  https://doi.org/10.3233/IDA-150751

    Article  Google Scholar 

  8. Chen, Y.Z., Shi, S., Chen, G.L., and Yu, Z.Y., Overlapping community discovery based on node hierarchy and label propagation gain, Pattern Recognit. Artif. Intell., 2015, vol. 28, no. 4, pp. 289–298.

    Google Scholar 

  9. Zhang, L., Pan, H., Su, Y., Zhang, X., and Niu, Y., A mixed representation-based multiobjective evolutionary algorithm for overlapping community detection, IEEE Trans. Cybern., 2017, vol. 47, no. 9, pp. 2703–2716. https://doi.org/10.1109/TCYB.2017.2711038

    Article  Google Scholar 

  10. Wang, W., Jiao, P., He, D., Jin, D., Pan, L., Gabrys, B., Autonomous overlapping community detection in temporal networks: A dynamic Bayesian nonnegative matrix factorization approach, Knowl.-Based Syst., 2016, vol. 110, pp. 121–134.  https://doi.org/10.1016/j.knosys.2016.07.021

    Article  Google Scholar 

  11. Li, Y., Wang, Y., Chen, J., Jiao, L., and Shang, R., Overlapping community detection through an improved multi-objective quantum-behaved particle swarm optimization, J. Heuristics, 2015, vol. 21, no. 4, pp. 549–575. https://doi.org/10.1007/s10732-015-9289-y

    Article  Google Scholar 

  12. Gregory, S., An algorithm to find overlapping community structure in networks, Knowledge Discovery in Databases: PKDD 2007, Kok, J.N., Koronaci, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., and Skowron, A., Eds., Lecture Notes in Computer Science, vol. 4702, Berlin: Springer, 2007, pp. 91–102. doi https://doi.org/10.1007/978-3-540-74976-9_12

    Book  Google Scholar 

  13. Zhang, X., Wang, C., Su, Y., Pan, L., and Zhang, H.-F., A fast overlapping community detection algorithm based on weak cliques for large-scale networks, IEEE Trans. Comput. Soc. Syst., 2017, vol. 4, no. 4, pp. 218–230. https://doi.org/10.1109/TCSS.2017.2749282

    Article  Google Scholar 

  14. Xie, J., Szymanski, B.K., and Liu, X., SLPA: Uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process, IEEE 11th Int. Conf. on Data Mining Workshops, Vancouver, 2011, IEEE, 2011, pp. 344–349.  https://doi.org/10.1109/ICDMW.2011.154

  15. Palla, G., Derényi, I., Farkas, I., and Vicsek, T., Uncovering the overlapping community structure of complex networks in nature and society, Nature, 2005, vol. 435, pp. 814–818.  https://doi.org/10.1038/nature03607

    Article  Google Scholar 

  16. Gregory, S., Finding overlapping communities in networks by label propagation, New J. Phys., 2010, vol. 12, p. 103018.  https://doi.org/10.1088/1367-2630/12/10/103018

    Article  MATH  Google Scholar 

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  1. School of Information Engineering, Suzhou Industrial Park Institute of Service Outsourcing, 215123, Suzhou, Jiangsu, China

    Jiqing Cao

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  1. Jiqing Cao
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Jiqing Cao Application of Overlapping Community Discovery Algorithm in Complex Network Big Data. Aut. Control Comp. Sci. 55 (Suppl 1), 8–15 (2021). https://doi.org/10.3103/S0146411621090042

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