This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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133
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Type:
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Contributed
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Date/Time:
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Monday, August 2, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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Abstract - #309189 |
Title:
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Change-Point Detection in Time Series of Attributed Graphs
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Author(s):
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Lucy F. Robinson*+ and Carey E. Priebe and Nam Lee
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Companies:
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The Johns Hopkins University and The Johns Hopkins University and The Johns Hopkins University
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Address:
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3400 N charles st , Baltimore, MD, 21218,
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Keywords:
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networks ;
change point ;
random graphs
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Abstract:
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We introduce a class of latent process models for time series of attributed graphs that is amenable to change point detection. Our goal is to detect a subpopulation of vertices exhibiting a change in behavior over time. Each edge has a location in continuous time and an attribute (e.g. the time and topic of an email message). We model the probability of edges and their attributes through time using a latent-space stochastic process associated with each vertex. A random dot product model is used to describe the dependency structure of the graph. We wish to detect shifts from homogeneity across vertices to a mixed population exhibiting anomalous behavior in a subset of vertices. The anomalous vertices' change is exhibited in altered behavior in both probability of interconnection and attribute distribution. Change point inference is performed using a modified likelihood ratio test.
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