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Applications of Bayesian Belief Networks in Social Network Analysis
D. Koelle, J. Pfautz, M. Farry, Z. Cox, G. Catto, and J. Campolongo
22nd Annual Conference on Uncertainty in Artificial Intelligence: UAI '06, Cambridge, MA (July, 2006).
In this paper, we discuss the use of Bayesian belief networks as a tool for enhancing social network analysis. Traditional social network analysis (SNA) primarily uses graph-theoretic algorithms to compute properties of nodes in a network. However, these algorithms assume a degree of completeness and reliability of the social network data, which cannot always be assured. Applying Bayesian belief networks to social network analysis provides additional capabilities for discovering new links and identifying particular nodes in the network that cannot be achieved using more traditional methods of social network analysis. We describe these applications of Bayesian belief networks and their implementation in a SNA tool.
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