Eigenspace-based anomaly detection in computer systems
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
ACM SIGKDD Explorations Newsletter
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
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Behavioral Inference across Cultures: Using Telephones as a Cultural Lens
IEEE Intelligent Systems
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The effect of the network structure on the dynamics of social and communication networks has been of interest in recent years. It has been observed that network properties such as neighborhood overlap, clustering coefficient, etc. influence the tie strengths and link persistence between individuals. In this paper we study the communication records (both phonecall and SMS) of 2 million anonymized customers of a large mobile phone company with 50 million interactions over a period of 6 months. Our major contributions are the following: (a) we analyze several structural properties in these call/SMS networks and the correlations between them; (b) we formulate a learning problem to determine whether existing links between users will persist in the future. Experimental results show that our method performs better than existing rule based methods; and (c) we propose a change-point detection method in user behaviors using eigenvalue analysis of various behavioral features extracted over time. Our analysis shows that change-points detected by our method coincide with the social events and festivals in our data.