How to model social nets – Exponential random graph models

Today we had a talk by Cyrus Hall of the University of Lugano. He proposed a p2p sampling algorithm to allow p2p admins to monitor and maintain their nets. He evaluated his algorithm against synthetic p2p topologies, which included kleinberg’s social net model and barabasi’s. For years now, it has been assumed that those traditional models reflect real social nets. However, after the talk, Damon pointed at the existence of exponential random graph models. Traditional models do not assume the knowledge of any global property (e.g., clustering coefficient) of a social network – they do the modeling without any input. By contrast, random graph models are able to model any type of social network on input of the network’s global properties. here is a paper titled “Recent developments in exponential random graph (p*) models for social networks” (pdf). Enjoy!

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