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Bayesian clustering of huge friendship networks

By Janne Aukia

Abstract

Because of the recent growth in popularity of social websites, such as MySpace, Facebook and Last.fm, there is an increasing interest in ways to analyze extremely large friendship networks with even millions of nodes. These huge networks provide a practical test ground for new network algorithms. The network analysis methods can also be applied to other networks than social networks, such as interactions between proteins and links between web pages. Social networks have typically structure: there are dense groups of nodes and some nodes have disproportionately many connections. The structure emerges, because friendships are not formed randomly. Instead, people tend to become friends with those who are similar to themselves. This can be called homophily. There are also other factors that guide the formation of friendships, such as geographical location and membership in common activities. The M0 algorithm finds clustering structure in networks with homophily by Bayesian statistical inference. The algorithm is based on a generative model for creating the edge

Year: 2007
OAI identifier: oai:CiteSeerX.psu:10.1.1.135.1629
Provided by: CiteSeerX
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