Abstract
In this paper, we present a multi-level empirical study of locality structures of several temporal call graphs containing both mobile and fixed-line call graphs emphasizing on comparing the patterns of these two types of call graphs. We investigate the topological patterns of the cohesive subgraphs in a mesoscopic scale, and we find several novel patterns in these call graphs. We study the correlation between the link weights and their localities. To our surprise, we can find there is nearly no correlation between link weights and their edge betweenness values. We also find that in the network of communities, communities’ sizes and betweenness centralities are highly positively correlated, which indicates large communities tend to be in the center of the call networks. Our analysis also suggests that ‘small-world’ phenomenon still exists in the community-based networks. We believe that our analysis results will help Telecom operators have better understanding of their customers.
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References
Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)
Barabási, A.L., Albert, R.: Emergence of Scaling in Random Networks. Science 286, 509–512 (1999)
Nanavati, A.A., Gurumurthy, S., et al.: On the Structural Properties of Massive Telecom Call Graphs: Finding and Implication. In: Proceedings of CIKM, pp. 435–444 (2006)
Nanavati, A.A., Singh, R., et al.: Analyzing the Structure and Evolution of Massive Telecom Graphs. IEEE Transactions on Knowledge and Data Engineering 50, 703–718 (2008)
Onnela, J.P., Saramäki, J., et al.: Structure and tie Strengths in mobile communication networks. Proc. Natl. Acad. Sci. 104, 7332–7336 (2007)
Leskovec, J., Lang, K.J., Dasgupta, A., Mahoney, M.W.: Statistical properties of community structure in large social and information networks. In: Proceeding of the 17th International Conference on World Wide Web, pp. 695–704 (2008)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. J. Proc. Natl. Acad. Sci. 12, 7821–7826 (2002)
Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–817 (2005)
Leung, I.X.Y., Hui, P., Liò, P., Crowcroft, J.: Towards real-time community detection in large networks. Phys. Rev. E. 79, 66107 (2009)
Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E. 76, 36106 (2007)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E. 69, 26113 (2004)
Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E. 69, 66133 (2004)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E. 70, 66111 (2004)
Fortunato, S., Barthélemy, M.: Resolution limit in community detection. Proc. Natl. Acad. Sci. 104, 36–41 (2007)
Kumpula, J.M., Saramäki, J., Kaski, K., Kertész, J.: Limited resolution in complex network community detection with Potts model approach. European Physical Journal B 56, 41–45 (2007)
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. 10008 (2008)
Fortunato, S.: Community detection in graphs. Physics Reports 486, 75–174 (2010)
Newman, M.E.J.: Assortative Mixing in Networks. Phys. Rev. Lett. 89, 208701 (2002)
Leskovec, J., Horvitz, E.: Planetary-scale views on a large instant-messaging network. In: Proceeding of the 17th International Conference on World Wide Web, pp. 915–924 (2008)
Ye, Q., Wu, B., et al.: TeleComVis: Exploring Temporal Communities in Telecom Networks. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, pp. 755–758. Springer, Bled Slovenia (2009)
Vladimir, B., Matjaž, Z.: An O(m) Algorithm for Cores Decomposition of Networks. CoRR arXiv.org/cs.DS/0310049 (2003)
Kwak, H., Choi, Y., et al.: Mining communities in networks: a solution for consistency and its evaluation. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference, pp. 301–314. ACM, New York (2009)
Spirin, V., Mirny, L.A.: Protein complexes and function modules in molecular networks. Proc. Natl. Acad. Sci. 100, 12123–12128 (2003)
Palla, G., Barabasi, A.L., Vicsek, T.: Quantifying social group evolution. Nature 446, 664–667 (2007)
Everett, M.G., Borgatti, S.P.: Analyzing Clique Overlap. Connections 21, 49–61 (1998)
Brandes, U.: A Faster Algorithm for Betweenness Centrality. Journal of Mathematical Sociology 25, 163–177 (2001)
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Ye, Q., Wu, B., Wang, B. (2010). Multiple Level Views on the Adherent Cohesive Subgraphs in Massive Temporal Call Graphs. In: Cao, L., Feng, Y., Zhong, J. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6440. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17316-5_42
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DOI: https://doi.org/10.1007/978-3-642-17316-5_42
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