FBM: A Flexible Random Walk Based Generative Model for Social Network

Abstract

This paper studied the static and dynamic characteristics of the real social networks as well as their proposed generative models, among which the Butterfly Model [1] is useful while not being flexible enough to generate the social networks with the expected power-law exponent. Therefore, a novel Flexible Butterfly Model (FBM) is proposed based on the Butterfly Model and combined with the Monte Carlo method and a Bayesian Graph Model for the training of the FBM Model is built in order to learn parameters from real social networks. Experiments have shown that the FBM model can adjust the law power exponent of the generated social network effectively by the introduced parameters. Meanwhile, the FBM model also maintains the vast majority of important characteristics that the Butterfly model has.