Exponential Random Graph Modeling for Micro-blog Network Analysis

Social network analysis is used to study complex networks by analyzing static structure and dynamic changes. Nowadays micro-blog as a new social media is becoming the most popular communication platform. How to capture micro-blog network structure especially dynamic structure poses more scientific interest. In this paper, we choose Chinese micro-blog, Sina weibo, on topic of diabetes as our test bed. We calculate degree, average shortest path, betweenness and clustering coefficient to analyze its static structure. More important works, we introduce a general model for micro-blog with directed network data, Exponential-family Random Graph Models (ERGMs), and illustrate the utility for modeling, analyzing and simulating micro-blog network. We also provide the goodness-of-fit approach to capture and reproduce the structure of the fitted micro-blog network. We demonstrate the characteristic results of average degree, diameter and clustering coefficient of diabetes micro-blog static structure. Parameters estimation of model, similarity results of simulated networks and observed networks, and goodness of fit analysis for micro-blog network are all illustrated that ERGMs are excellent methods to deeply capture the complex network structure.