Abstract

In recent years, Social Network Service is a novel, popular way to make friends andconvey information online. Therefore, the analysis of network data has attracted a lot ofattention. It is an area that is rapidly growing, both with Statistics and Computer Science.This paper rst provides a summary of statistical methods used in network data analysis,including basic denitions, measurements, and descriptive statistics. We then introduce theExponential Random Graph Model to t network data. Secondly, we dig into a more specicarea of network analysis: Community Detection. We discuss two dierent methods to explorethe community stucture, one is Louvain algorithm and the other is Mixed MembershipStochastic Blockmodels. After that, we combine the community identication with a two-stage user similarity algorithm to build a friend recommendation method. In the empiricalstudy section, we apply this method to a real-world dataset and evaluate its performacethrough specic measurements.