The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Statistics and data mining techniques for lifetime value modeling
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithms for estimating relative importance in networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling and predicting personal information dissemination behavior
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Using Egocentric Networks to Understand Communication
IEEE Internet Computing
On the structural properties of massive telecom call graphs: findings and implications
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Analyzing the Structure and Evolution of Massive Telecom Graphs
IEEE Transactions on Knowledge and Data Engineering
A visual-analytic toolkit for dynamic interaction graphs
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Bridging the gap: complex networks meet information and knowledge management
Proceedings of the 18th ACM conference on Information and knowledge management
A CRM system for social media: challenges and experiences
Proceedings of the 22nd international conference on World Wide Web
Using social networks to enhance customer relationship management
Proceedings of the Fifth International Conference on Management of Emergent Digital EcoSystems
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The structure of customer communication network provides us a natural way to understand customers' relationships. Traditional customer relationship management (CRM) methods focus on various customer profitability models, and they are short of ways to understand the social interactions. Graph mining and social network analysis provide ways to understand the relationships between customers, and there are already a few applications in CRM using these methods. To transform the traditional CRM methods from individuals to social groups, we propose a novel technical framework (GCRM) to manage the social groups in massive telecom call graphs. Our framework is based on a series of newly emerged methods for social network analysis, such as group detecting, group evolution tracking and group life-cycle modeling in telecom applications. We analyze the relationships between social groups and propose a method to find potential customers in these groups. To evaluate GCRM, we present a comprehensive study to explore the group evolutions in real-world massive telecom call graphs. Empirical results show that by taking this framework, analysts can gain deeper insights into the communication patterns of social groups and their evolutionary patterns which makes the management of these social groups much easier in real-world telecom applications.