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Visualizing and Understanding Large-Scale Bayesian Networks
The AAAI-11 Workshop on Scalable Integration of Analytics and Visualization (2011)
  • Michele Cossalter, Carnegie Mellon University
  • Ole J Mengshoel, Carnegie Mellon University
  • Ted Selker, Carnegie Mellon University

Bayesian networks are a theoretically well-founded approach to represent large multi-variate probability distributions, and have proven useful in a broad range of applications. While several software tools for visualizing and editing Bayesian networks exist, they have important weaknesses when it comes to enabling users to clearly understand and compare conditional probability tables in the context of network topology, especially in large-scale networks. This paper describes a system for improving the ability for computers to work with people to develop intelligent systems through the construction of high-performing Bayesian networks. We describe NetEx, a tool developed as a Cytoscape plugin, which allows a user to visually inspect and compare details concerning multiple nodes in a Bayesian network while maintaining awareness of their network context. It uses a “thought bubble line” to connect nodes in a graph representation and their internal information at the side of the graph. The tool seeks to improve the ability of experts to analyze and debug large Bayesian network models, and to help people to understand how alternative algorithms and Bayesian networks operate, providing insights into how to improve them.

Publication Date
August 7, 2011
Citation Information
Michele Cossalter, Ole J Mengshoel and Ted Selker. "Visualizing and Understanding Large-Scale Bayesian Networks" The AAAI-11 Workshop on Scalable Integration of Analytics and Visualization (2011)
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