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

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Book cover 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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References

  1. Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. U.S.A. 99(12), 7821–7826 (2002). USA

    Article  MathSciNet  MATH  Google Scholar 

  2. Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E: Stat. Nonlin. Soft Matter Phys. 69, 066133 (2004)

    Article  Google Scholar 

  3. Blondel, V.D., Guillaume, J.L.R., Lambiotte, L.E.: Fast unfolding of communities in large networks. J. Stat. Mech.: Theory Exp. P10008 (2008)

    Google Scholar 

  4. Fortunato, S., Castellano, C.: Community structure in graphs. In: Meyers, R.A. (ed.) Computational Complexity, pp. 490–512. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  5. Yang, B., Liu, J., Feng, J.: On the spectral characterization and scalable mining of network communities. IEEE Trans. Knowl. Data Eng. 24(2), 326–337 (2012)

    Article  MathSciNet  Google Scholar 

  6. Morarescu, I.C., Girard, A.: Opinion dynamics with decaying confidence: application to community detection in graphs. IEEE Trans. Autom. Control 56(8), 1862–1873 (2011). Institute of Electrical and Electronics Engineers

    Article  MathSciNet  Google Scholar 

  7. Newman, M.E., Leicht, E.A.: Mixture models and exploratory analysis in networks. Proc. Natl. Acad. Sci. U.S.A. 104(23), 9564–9569 (2007)

    Article  MATH  Google Scholar 

  8. Wu, Z., Cao, J., Zhu, G., Yin, W., Cuzzocrea, A., Shi, J.: Detecting overlapping communities in poly-relational networks. World Wide Web 18, 1–18 (2015)

    Article  Google Scholar 

  9. Wei, F., Qian, W., Wang, C., Zhou, A.: Detecting overlapping community structures in networks. World Wide Web 12, 235–261 (2009)

    Article  Google Scholar 

  10. Palla, G., Derenyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)

    Article  Google Scholar 

  11. Xia, Z., Bu, Z.: Community detection based on a semantic network. Knowl.-Based Syst. 26, 30–39 (2012)

    Article  Google Scholar 

  12. Ruan, Y., Fuhry, D., Parthasarathy, S.: Efficient community detection in large networks using content and links. In: 22nd International Conference on World Wide Web, pp. 1089–1098. ACM, New York (2013)

    Google Scholar 

  13. Lin, W., Kong, X., Yu, P.S., Wu, Q., Jia, Y., Li, C.: Community detection in incomplete information networks. In: 21st International Conference on World Wide Web, pp. 341–350. ACM, New York (2012)

    Google Scholar 

  14. Papadopoulos, S., Kompatsiaris, Y., Vakali, A., Spyridonos, P.: Community detection in social media. Data Min. Knowl. Discovery 24, 515–554 (2012)

    Article  Google Scholar 

  15. Peng, D., Lei, X., Huang, T.: DICH: a framework for discovering implicit communities hidden in tweets. World Wide Web 18, 1–24 (2014)

    Google Scholar 

  16. Modani, N., Nagar, S., Shannigrahi, S., Gupta, R., Dey, K., Goyal, S., Nanavati, A.A.: Like-minded communities: bringing the familiarity and similarity together. World Wide Web 17, 899–919 (2014)

    Article  Google Scholar 

  17. Lin, Y.R., Sun, J., Castro, P., Konuru, R., Sundaram, H., Kelliher, A.: MetaFac: community discovery via relational hypergraph factorization. In: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 527–536. ACM, New York (2009)

    Google Scholar 

  18. Maiya, A.S., Berger-Wolf, T.Y.: Sampling community structure. In: 19th International Conference on World Wide Web, pp. 701–710. ACM, New York (2010)

    Google Scholar 

  19. Djidjev, Hristo N.: A scalable multilevel algorithm for graph clustering and community structure detection. In: Aiello, William, Broder, Andrei, Janssen, Jeannette, Milios, Evangelos E. (eds.) WAW 2006. LNCS, vol. 4936, pp. 117–128. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  20. Ngonmang, B., Tchuente, M., Viennet, E.: Local community identification in social networks. Parallel Process. Lett. 22, 1240004 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  21. Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E: Stat. Nonlin. Soft Matter Phys. 76, 036106 (2007)

    Article  Google Scholar 

  22. Lin, Z., Zheng, X., Xin, N., Chen, D.: CK-LPA: efficient community detection algorithm based on label propagation with community kernel. Phys. A: Stat. Mech. Appl. 416, 386–399 (2014)

    Article  Google Scholar 

  23. Leung, I.X., Hui, P., Lio, P., Crowcroft, J.: Towards real-time community detection in large networks. Phys. Rev. E: Stat. Nonl. Soft Matter Phys. 79, 066107 (2009)

    Article  Google Scholar 

  24. Šubelj, L., Bajec, M.: Unfolding network communities by combining defensive and offensive label propagation. arXiv preprint arXiv:1103.2596 (2011)

  25. Barber, M.J., Clark, J.W.: Detecting network communities by propagating labels under constraints. Phys. Rev. E: Stat. Nonl. Soft Matter Phys. 80, 026129 (2009)

    Article  Google Scholar 

  26. Liu, X., Murata, T.: Advanced modularity-specialized label propagation algorithm for detecting communities in networks. Phys. A: Stat. Mech. Appl. 389, 1493–1500 (2010)

    Article  Google Scholar 

  27. Gregory, S.: Finding overlapping communities in networks by label propagation. New J. Phys. 12, 103018 (2010)

    Article  Google Scholar 

  28. Xie, J., Szymanski, B.K.: Towards linear time overlapping community detection in social networks. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012. LNCS, vol. 7302, pp. 25–36. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  29. Wu, Z.H., Lin, Y.F., Gregory, S., Wan, H.Y., Tian, S.F.: Balanced multi-label propagation for overlapping community detection in social networks. J. Comput. Sci. Technol. 27, 468–479 (2012)

    Article  MathSciNet  Google Scholar 

  30. Ugander, J., Backstrom, L.: Balanced label propagation for partitioning massive graphs. In: 6th ACM International Conference on Web Search And Data Mining, pp. 507–516. ACM, New York (2013)

    Google Scholar 

  31. Šubelja, L., Bajec, M.: Robust network community detection using balanced propagation. Eur. Phys. J. B 81, 353–362 (2011)

    Article  Google Scholar 

  32. Kuzmin, K., Shah, S.Y., Szymanski, B.K.: Parallel overlapping community detection with SLPA. In: 2013 International Conference on Social Computing (SocialCom), pp. 204–212. IEEE Press (2013)

    Google Scholar 

  33. Li, S., Lou, H., Jiang, W., Tang, J.: Detecting community structure via synchronous label propagation. Neurocomputing 151, 1063–1075 (2015)

    Article  Google Scholar 

  34. Cordasco, G., Gargano, L.: Community detection via semi-synchronous label propagation algorithms. In: 2010 IEEE International Workshop on Business Applications of Social Network Analysis (BASNA), pp. 1–8. IEEE (2010)

    Google Scholar 

  35. Newman, M.E., Peixoto, T.P.: Generalized communities in networks. Phys. Rev. Lett. 115, 088701 (2015)

    Article  Google Scholar 

  36. Hoff, P.D., Raftery, A.E., Handcock, M.S.: Latent space approaches to social network analysis. J. Am. Stat. Assoc. 97, 1090–1098 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  37. Karrer, B., Newman, M.E.: Stochastic blockmodels and community structure in networks. Phys. Rev. E: Stat. Nonl. Soft Matter Phys. 83, 016107 (2011)

    Article  MathSciNet  Google Scholar 

  38. Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E: Stat. Nonl. Soft Matter Phys. 69, 026113 (2004)

    Article  Google Scholar 

  39. Danon, L., Diaz-Guilera, A., Duch, J., Arenas, A.: Comparing community structure identification. J. Stat. Mech.: Theory Exp. P09008 (2005)

    Google Scholar 

Download references

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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  • Online ISBN: 978-981-10-2993-6

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