Bayes net graphs to understand co-authorship networks?

Improvements in data collection and the birth of online communities made it possible to obtain very large social networks (graphs). Several communities have been involved in modeling and analyzing these graphs. Usage of graphical models, such as Bayesian Networks (BN), to analyze massive data has become increasingly popular, due to their scalability and robustness to noise. In the literature BNs are primarily used for compact representation of joint distributions and to perform inference, i.e. answer queries about the data. In this work we learn Bayes Nets using the previously proposed SBNS algorithm [14]. We look at the learned networks for the purpose of analyzing the graph structure itself. We also point out a few improvements over the SBNS algorithm. The usefulness of Bayes Net structures to understand social networks is an open area. We discuss possible interpretations using a small subgraph of the Medline publications and hope to provoke some discussion and interest in further analysis.

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