Mining Mobile Phone Messages in Mobile Social Network

摘要

A mobile social network plays an essential role as the spread of information and relationship. Mining the popular P2P messages in a short period of time is very valuable. Traditional mining method is not suitable for this very large scale dataset. In this paper, we present a mining approach based on MapReduce parallel framework. We use our metric to analyze point-to-point (p2p) messages within an organization to extract social hierarchy. We analyze the behavior of the communication patterns with taking into account the actual communication messages sent by users. Experimental results show that the final dataset of popular messages is very small with high sending coverage ratio. Empirical studies on a large real-world mobile social network show that performance of our algorithm.