Using Egocentric Networks to Understand Communication
IEEE Internet Computing
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
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
Social ties and their relevance to churn in mobile telecom networks
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Analyzing the Structure and Evolution of Massive Telecom Graphs
IEEE Transactions on Knowledge and Data Engineering
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mobile call graphs: beyond power-law and lognormal distributions
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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The structure of customer communication network provides us the insights into the function of customers' relationships. In this paper, we use egocentric social network to explore how people manage their personal and group communications over time. Our primary goal is that our findings can provide business insights and help devise strategies for telecom service providers. We are interested in tracking changes in large-scale mobile networks and examining the evolution processes of customer egocentric networks. By defining several statistical metrics, we can investigate the egocentric networks' evolution trends and their communication patterns. We explore several temporal real-world mobile call graphs and find an interesting phenomenon in these temporal networks which is the neighboring vertices' egocentric networks have assortiative evolution trends. By taking a visual analytics approach, we track the changes in the customer egocentric networks and explore some highly correlated customers' egocentric networks visually. We detect several interesting communication patterns by visualizing the egocentric networks which may give us more hints on customers' communication trends in their egocentric networks.