Bayes Net Graphs to Understand Coauthorship
Networks?
Link KDD'05, (2005)Abstract
Improvements in data collection and the birth of online com-
munities made it possible to obtain very large social net-
works (graphs). Several communities have been involved
in modeling and analyzing these graphs. Usage of graph-
ical 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 distribu-
tions and to perform inference, i.e. answer queries about
the data. In this work we learn Bayes Nets using the previ-
ously 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 under-
stand social networks is an open area. We discuss possible
interpretations using a small subgraph of the Medline pub-
lications and hope to provoke some discussion and interest
in further analysis.
