Abstract
In Mobile Social Network Analysis, mobile users interaction pattern change frequently and hence it is very hard to detect their changing patterns because humans posses an extremely high degree of randomness in their calling behavior. To identify regularity in such random behavior, we propose a new method using network attributes to find periodic or near periodic graphs in dynamic social networks. We try to analyze real-world mobile social networks and extract its periodicity through a simple practical and efficient method using effective network attributes of the social network. We demonstrate the applicability of our approach on real-world networks and extract meaningful and interesting periodic interaction patterns. This helps in defining targeted business models in cellular communication arena.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Berger-Wolf, T.F., Saia, J.: A framework for analysis of dynamic social networks. In: The 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 20–23, Philadelphia, PA, USA (2006)
Cao, L.: In-depth behavior understanding and use: the behavior informatics approach. Inf. Sci. 180, 3067–3085 (2010)
Clauset, A., Eagle, N.: Persistence and periodicity in a dynamic proximity network. In: DIMACS Workshop on Computational Methods for Dynamic Interaction Networks, pp. 1–5 (2007)
Dasgupta, K., Singh, R., Viswanathan, B., Chakraborthy, D., Joshi, A., Mukherje, S., Nanavati, A.: Social ties and their relevance to churn in mobile telecom networks. In: The 11th International Conference on Extending Database Technology Advances in Database Technology (EDBT), France (2008)
Eagle, N., Pentland, A., Lazer, D.: Inferring social network structure using mobile phone data. In: The National Academy of Sciences, vol. 106(36), pp. 15274–15278 (2009)
Lahiri, M., Berger-Wolf, T.F.: Mining periodic behavior in dynamic social networks. In: 8th IEEE International Conference on Data Mining, December 15–19, pp. 373–382 (2008)
Robardet, C.: Constraint-based pattern mining in dynamic graphs. In: The Ninth IEEE International Conference on Data Mining (ICDM), Fl, USA, pp. 950–955 (2009)
Saravanan, M., Prasad, G., Karishma, S., Suganthin, D.: Labeling communities using structural properties. In: International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Odense, Denmark, pp. 217–224 (2010)
Tantipathananandh, C., Berger-Wolf, T.F., Kempe, D.: A framework for community identification in dynamic social networks. In: The 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 12–15, San Jose, California, USA (2007)
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, New York (1994)
Weiss, G.: Data Mining in Telecommunications, Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, pp. 1189–1201. Kluwer Academic, Dordrecht (2005)
Acknowledgements
This work was supported by Ericsson R&D, Chennai. We would like to thank our team members and managers, R&D Head for moral support and encouragement to complete this research work. We propose our sincere thanks to Prasad Garigipati, Anand Varadarajan, and Lennart Isaksson, for their excellent support in conceptualizes our research idea.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag London
About this chapter
Cite this chapter
Polepally, A., Mohan, S. (2012). Behavior Analysis of Telecom Data Using Social Networks Analysis. In: Cao, L., Yu, P. (eds) Behavior Computing. Springer, London. https://doi.org/10.1007/978-1-4471-2969-1_18
Download citation
DOI: https://doi.org/10.1007/978-1-4471-2969-1_18
Publisher Name: Springer, London
Print ISBN: 978-1-4471-2968-4
Online ISBN: 978-1-4471-2969-1
eBook Packages: Computer ScienceComputer Science (R0)