Behavior analysis of telecom data using social networks analysis

Abstract

In Mobile Social Network Analysis, mobile users interaction pattern change frequently and hence it is very hard to detect their changing patterns because humans posses an extremely high degree of randomness in their calling behavior. To identify regularity in such random behavior, we propose a new method using network attributes to find periodic or near periodic graphs in dynamic social networks. We try to analyze real-world mobile social networks and extract its pe-riodicity through a simple practical and efficient method using effective network attributes of the social network. We demonstrate the applicability of our approach on real-world networks and extract meaningful and interesting periodic interaction patterns. This helps in defining targeted business models in cellular communication arena.