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		<rdfs:label><![CDATA[Analyzing the Structure and Evolution of Massive Telecom Graphs]]></rdfs:label>
		<pub:title><![CDATA[Analyzing the Structure and Evolution of Massive Telecom Graphs]]></pub:title>
		<pub:publishedOn rdf:datatype="&xsd;dateTime">2008-03-31T00:00:00-05:00</pub:publishedOn>
		<pub:abstract><![CDATA[With the 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 Treasure-Hunt model to describe the shape of mobile call graphs. Moreover, we 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 study the properties of the SMS graph. Our analysis indicates several interesting similarities and 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.]]></pub:abstract>
		<pub:pages><![CDATA[703-718]]></pub:pages>
		<pub:number><![CDATA[5]]></pub:number>
		<pub:volume><![CDATA[20]]></pub:volume>
		<pub:counter>3618</pub:counter>
		<pub:tag><![CDATA[social network]]></pub:tag>
		<pub:booktitle><![CDATA[IEEE Transactions on Knowledge and Data Engineering]]></pub:booktitle>
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