This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 133
Type: Contributed
Date/Time: Monday, August 2, 2010 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract - #309189
Title: Change-Point Detection in Time Series of Attributed Graphs
Author(s): Lucy F. Robinson*+ and Carey E. Priebe and Nam Lee
Companies: The Johns Hopkins University and The Johns Hopkins University and The Johns Hopkins University
Address: 3400 N charles st , Baltimore, MD, 21218,
Keywords: networks ; change point ; random graphs

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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