Extracting Regular Behaviors from Social Media Networks

Abstract

Social media network analysis has become very popular in recent years. How do real networks evolve over time? What are the normal evolving behaviors in a social media network? In order to extract behaviors occurring regularly to reveal the microscopic evolving properties in social networks, the evolving process of networks is modeled as stochastic states transition, and the evolving behaviors are described as topological structure changes of a series of sub graphs. Then, based on Maximal Frequent Sub graph mining technology, RB Miner (Regular Behaviors Miner) algorithm is proposed to identify such regular behaviors in network dataset. The empirical evaluation using both synthetic and real dataset verifies that the proposed algorithm is valid, and the regular behavior patterns show more dynamic information hidden in evolving social networks than normal frequent sub graph patterns.