Abstract
Modularity function is a widely-used criterion to evaluate the strength of community structure in community detection. In this paper, we propose a modularity maximization method for detecting communities, based on genetic algorithm and random walk model, and propose a new community structure encoding method for networks. First, the random walk model was applied to calculate the similarity between nodes, resulting in a weighted matrix as derived from the original adjacency matrix. According to the nearest neighbor-based similarity representation provisional, a weighted network connection structure was then coded into a chromosome. The genetic algorithm modified the structure of a predefined number of chromosomes and computed the corresponding modularity, ultimately yielding the maximum value of modularity as it corresponds to community structure and number of communities. We tested this method on a series of real social networks. Compared with several state-of-the-art methods, the novel method obtained both greater modularity value. Thus, results by the proposed method are more practical, since this method does not require specified number of communities at the outset of community partition. Here, the optimal number of communities and community structures are automatically determined.
Keywords
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Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. U.S.A. 99, 7821–7826 (2002)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)
Humphries, M.D.: Spike-train communities: finding groups of similar spike trains. J. Neurosci. 31(6), 2321–2336 (2011)
Langone, R., Mall, R., Suykens, J.A.K.: Soft Kernel spectral clustering. In: Proceedings of the IJCNN, Dallas, Texas, pp. 1028–1035 (2013)
Langone, R., Mall, R., Vandewalle, J., Suykens, J.A.K.: Discovering cluster dynamics using kernel spectral methods. In: Lü, J., Yu, X., Chen, G., Yu, W. (eds.) Complex Systems and Networks. UCS, pp. 1–24. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-47824-0_1
Chan, E.Y.K., Yeung, D.Y.: A convex formulation of modularity maximization for community detection. In: International Joint Conference on Artificial Intelligence, pp. 2218–2225. AAAI Press (2011)
Brandes, U., et al.: Maximizing modularity is hard. arXiv preprint physics/0608255 (2006)
Duch, J., Arenas, A.: Community detection in complex networks using extremal optimization. Phys. Rev. E 72(2), 027104 (2005)
Tasgin, M., Herdagdelen, A., Bingol, H.: Community detection in complex networks using genetic algorithms. arXiv preprint arXiv:0711.0491 (2007)
Pizzuti, C.: GA-Net: a genetic algorithm for community detection in social networks. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 1081–1090. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87700-4_107
Park, Y.J., Song, M.S.: A genetic algorithm for clustering problems. In: 3rd Annual Conference, pp. 568–575. Morgan, Kaufmann (1989)
Amelio, A., Pizzuti, C.: A genetic algorithm for color image segmentation. In: Esparcia-Alcázar, A.I. (ed.) EvoApplications 2013. LNCS, vol. 7835, pp. 314–323. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37192-9_32
Amelio, A., Pizzuti, C.: A new evolutionary-based clustering framework for image databases. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2014. LNCS, vol. 8509, pp. 322–331. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07998-1_37
Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69, 066133 (2004)
Pons, P., Latapy, M.: Computing communities in large networks using random walks. In: Yolum, P., Güngör, T., Gürgen, F., Özturan, C. (eds.) ISCIS 2005. LNCS, vol. 3733, pp. 284–293. Springer, Heidelberg (2005). https://doi.org/10.1007/11569596_31
Valencia, M., Pastor, M.A., Artieda, J., Martinerie, J., Chavez, M.: Complex modular structure of large-scale brain networks. Chaos Interdiscip. J. Nonlinear Sci. 19, 023119 (2009)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22, 888–905 (2000)
Hruschka, E.R.: A genetic algorithm for cluster analysis. Intell. Data Anal. 7, 15–25 (2003)
Martelot, E.L., Hankin, C.: Multi-scale community detection using stability as optimisation criterion in a greedy algorithm. KDIR, pp. 216–225 (2011)
Psorakis, I., Roberts, S., Ebden, M., Sheldon, B.: Overlapping community detection using Bayesian non-negative matrix factorization. Phys. Rev. E 83(2), 066114 (2011)
Ruan, J., Zhang, W.: Identifying network communities with a high resolution. Phys. Rev. E 77, 016104 (2008)
Good, B.H., Montjoye, Y.A.D., Clauset, A.: Performance of modularity maximization in practical contexts. Phys. Rev. E Stat. Nonlin. Soft Matter. Phys. 81(2), 046106 (2009)
Fortunato, S., Barthélemy, M.: Resolution limit in community detection. Proc. Natl. Acad. Sci. 104, 36–41 (2007)
Guimerà, R., Danon, L., Díazguilera, A., Giralt, F., Arenas, A.: Self-similar community structure in a network of human interactions. Phys. Rev. E Stat. Nonlin. Soft Matter. Phys. 68, 065103 (2003)
Bu, D., et al.: Topological structure analysis of the protein-protein interaction network in budding yeast. Nucleic Acids Res. 31(9), 2443–2450 (2003)
Acknowledgments
This study was supported by the National Natural Science Foundation of China (Project No. 61375122 and Project No. 61572239). Scientific Research Foundation for Advanced Talents of Jiangsu University (Project No. 14JDG040). Postgraduate Research & Practice Innovation Program of Jiangsu Province (Project No. SJCX18_0741).
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Lu, H., Yao, Q. (2018). Modularity Maximization for Community Detection Using Genetic Algorithm. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_41
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