Visualizing and Understanding Large-Scale Bayesian Networks

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.

[1]  David C. Wilkins,et al.  Controlled generation of hard and easy Bayesian networks: Impact on maximal clique size in tree clustering , 2006, Artif. Intell..

[2]  Ole J. Mengshoel,et al.  Diagnosing Intermittent and Persistent Faults using Static Bayesian Networks , 2010 .

[3]  Ole J. Mengshoel,et al.  Probabilistic Model-Based Diagnosis: An Electrical Power System Case Study , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[4]  Ole J. Mengshoel,et al.  Diagnosing Faults in Electrical Power Systems of Spacecraft and Aircraft , 2008, AAAI.

[5]  Ewart R. Carson,et al.  A Model-Based Approach to Insulin Adjustment , 1991, AIME.

[6]  William Ribarsky,et al.  Multi-Focused Geospatial Analysis Using Probes , 2008, IEEE Transactions on Visualization and Computer Graphics.

[7]  Martin Neil,et al.  Building large-scale Bayesian networks , 2000, The Knowledge Engineering Review.

[8]  Kim Marriott,et al.  Layout of Bayesian Networks , 2005, ACSC.

[9]  Chris Weaver.  Building Highly-Coordinated Visualizations in Improvise , 2004 .

[10]  Tsai-Ching Lu,et al.  Collaborative Development of Large Bayesian Networks , 2006, 2006 IEEE Autotestcon.

[11]  John E. Desmedt,et al.  Computer Aided Electromyography and Expert Systems , 1989 .

[12]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[13]  Ah-Hwee Tan,et al.  Explaining inferences in Bayesian networks , 2008, Applied Intelligence.

[14]  R. Amant,et al.  A Visualization Technique for Bayesian Modeling , 2005 .

[15]  Colin Ware,et al.  Zooming versus multiple window interfaces: Cognitive costs of visual comparisons , 2006, TCHI.

[16]  Ole J. Mengshoel,et al.  Understanding the scalability of Bayesian network inference using clique tree growth curves , 2010, Artif. Intell..

[17]  CockburnAndy,et al.  A review of overview+detail, zooming, and focus+context interfaces , 2009 .

[18]  Luigi Portinale,et al.  Bayesian networks in reliability , 2007, Reliab. Eng. Syst. Saf..

[19]  G. Grinstein,et al.  Visualizing Graphical Probabilistic Models , 2006 .

[20]  Ole J. Mengshoel,et al.  Advanced Diagnostics and Prognostics Testbed , 2007 .

[21]  Kim Marriott,et al.  Hi-Trees and Their Layout , 2011, IEEE Transactions on Visualization and Computer Graphics.

[22]  M. Sheelagh T. Carpendale,et al.  VisLink: Revealing Relationships Amongst Visualizations , 2007, IEEE Transactions on Visualization and Computer Graphics.

[23]  Marek J. Druzdzel,et al.  SMILE: Structural Modeling, Inference, and Learning Engine and GeNIE: A Development Environment for Graphical Decision-Theoretic Models , 1999, AAAI/IAAI.

[24]  Jung-Fu Cheng,et al.  Turbo Decoding as an Instance of Pearl's "Belief Propagation" Algorithm , 1998, IEEE J. Sel. Areas Commun..

[25]  Allison Woodruff,et al.  Guidelines for using multiple views in information visualization , 2000, AVI '00.

[26]  Jim E. Greer,et al.  Visualization of Bayesian Belief Networks , 1999 .

[27]  Haiqin Wang,et al.  User Interface Tools for Navigation in Conditional Probability Tables and Elicitation of Probabilities in Bayesian Networks , 2000, UAI.

[28]  Kristian G. Olesen,et al.  HUGIN - A Shell for Building Bayesian Belief Universes for Expert Systems , 1989, IJCAI.

[29]  Sheng Ma,et al.  Accuracy vs. efficiency trade-offs in probabilistic diagnosis , 2002, AAAI/IAAI.

[30]  Adnan Darwiche,et al.  Modeling and Reasoning with Bayesian Networks , 2009 .

[31]  S. Rahman.  Reliability Engineering and System Safety , 2011 .

[32]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[33]  Finn Verner Jensen,et al.  MUNIN: an expert EMG assistant , 1988 .

[34]  Benjamin B. Bederson,et al.  A review of overview+detail, zooming, and focus+context interfaces , 2009, CSUR.

[35]  D. Heckerman,et al.  ,81. Introduction , 2022 .

[36]  Ole J. Mengshoel,et al.  Developing Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft , 2009, IJCAI 2009.

[37]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[38]  Pourang Irani,et al.  Visualizing Causal Semantics Using Animations , 2007, IEEE Transactions on Visualization and Computer Graphics.

[39]  Steen Andreassen,et al.  Computer-Aided Electromyography and Expert Systems , 1989 .