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With ever growing competition in telecommunications markets, operators have to increasingly rely on business intelligence to offer the right incentives to their customers. Existing approaches for telecom business intelligence have almost solely focused on the individual behavior of customers. In this paper, we use the Call Detail Records of a mobile operator to construct Call graphs, that is, graphs induced by people calling each other. We determine the structural properties of these graphs and also introduce the {\sl Treasure-Hunt} model to describe the shape of mobile call graphs. We also determine how the structure of these call graphs evolve over time. Finally, since Short Messaging Service (SMS) is becoming a preferred mode of communication among many sections of the society we also study the properties of the SMS graph. Our analysis indicates several interesting similarities as well as differences between the SMS graph and the corresponding call graph. We believe that our analysis techniques can allow telecom operators to better understand the social behavior of their customers, and potentially provide major insights for designing effective incentives.
Data mining Database Applications, Database Management, Information Technology and Systems, Graph algorithms, Telecom Call Graphs, Social Network Analysis
Dipanjan Chakraborty, Siva Gurumurthy, Amit Anil Nanavati, Rahul Singh, Koustuv Dasgupta, Sougata Mukherjea, Anupam Joshi, Gautam Das, "Analyzing the Structure and Evolution of Massive Telecom Graphs", IEEE Transactions on Knowledge & Data Engineering, vol. 20, no. , pp. 703-718, May 2008, doi:10.1109/TKDE.2007.190733
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