| On the structural properties of massive telecom call graphs: findings and implications |
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Conference on Information and Knowledge Management
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Proceedings of the 15th ACM international conference on Information and knowledge management
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Arlington, Virginia, USA
SESSION: Graphs and trees
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Pages: 435 - 444
Year of Publication: 2006
ISBN:1-59593-433-2
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Authors
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Amit A. Nanavati
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IBM India Research Laboratory, New Delhi, India
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Siva Gurumurthy
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IBM India Research Laboratory, New Delhi, India
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Gautam Das
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IBM India Research Laboratory, New Delhi, India
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Dipanjan Chakraborty
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IBM India Research Laboratory, New Delhi, India
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Koustuv Dasgupta
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IBM India Research Laboratory, New Delhi, India
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Sougata Mukherjea
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IBM India Research Laboratory, New Delhi, India
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Anupam Joshi
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University of Maryland, Baltimore County, Maryland
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Downloads (6 Weeks): 19, Downloads (12 Months): 231, Citation Count: 4
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ABSTRACT
With ever growing competition in telecommunications markets, operators have to increasingly rely on business intelligence to offer the right incentives to their customers. Toward this end, existing approaches have almost solely focussed on the individual behaviour of customers. Call graphs, that is, graphs induced by people calling each other, can allow telecom operators to better understand the interaction behaviour of their customers, and potentially provide major insights for designing effective incentives.In this paper, we use the Call Detail Records of a mobile operator from four geographically disparate regions to construct call graphs, and analyse their structural properties. Our findings provide business insights and help devise strategies for Mobile Telecom operators. Another goal of this paper is to identify the shape of such graphs. In order to do so, we extend the well-known reachability analysis approach with some of our own techniques to reveal the shape of such massive graphs. Based on our analysis, we introduce the Treasure-Hunt model to describe the shape of mobile call graphs. The proposed techniques are general enough for analysing any large graph. Finally, how well the proposed model captures the shape of other mobile call graphs needs to be the subject of future studies.
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[doi> 10.1145/335305.335326]
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CITED BY 4
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Mukund Seshadri , Sridhar Machiraju , Ashwin Sridharan , Jean Bolot , Christos Faloutsos , Jure Leskove, Mobile call graphs: beyond power-law and lognormal distributions, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2008, Las Vegas, Nevada, USA
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Miklos Kurucz , Andras Benczur , Karoly Csalogany , Laszlo Lukacs, Spectral clustering in telephone call graphs, Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, p.82-91, August 12-12, 2007, San Jose, California
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Koustuv Dasgupta , Rahul Singh , Balaji Viswanathan , Dipanjan Chakraborty , Sougata Mukherjea , Amit A. Nanavati , Anupam Joshi, Social ties and their relevance to churn in mobile telecom networks, Proceedings of the 11th international conference on Extending database technology: Advances in database technology, March 25-29, 2008, Nantes, France
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Akshay Java , Xiaodan Song , Tim Finin , Belle Tseng, Why we twitter: understanding microblogging usage and communities, Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, p.56-65, August 12-12, 2007, San Jose, California
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