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Activity Number:
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174
- Dynamic Network Modeling
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Type:
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Contributed
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Date/Time:
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Monday, July 31, 2017 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #324145
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View Presentation
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Title:
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Dynamic Network Monitoring Using Random Graph Theory and Statistical Process Monitoring
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Author(s):
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James D. Wilson* and Nathaniel Stevens and William H Woodall
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Companies:
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University of San Francisco and University of San Francisco and Virginia Tech
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Keywords:
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statistical process monitoring ;
change point ;
stochastic block model ;
random graph theory
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Abstract:
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In many applications it is of interest to identify anomalous behavior within a dynamic interacting system. Such anomalous interactions are reflected by structural changes in the network representation of the system. We propose and investigate the use of a dynamic version of the degree corrected stochastic block model (DCSBM) to model and monitor dynamic networks that undergo a significant structural change. We apply statistical process monitoring techniques to the estimated parameters of the DCSBM to identify significant structural changes in the network. Application of our surveillance strategy to the dynamic U.S. Senate co-voting network reveals that we are able to detect significant changes in the network that reflect both times of cohesion and times of polarization among Republican and Democratic party members. These findings provide valuable insight about the evolution of the bipartisan political system in the United States. The DCSBM approach is an example of a more general framework that combines parametric random graph models and statistical process monitoring techniques for network surveillance.
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Authors who are presenting talks have a * after their name.
