Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 388 - New Development of Change-Point Methods
Type: Invited
Date/Time: Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract #316669
Title: Simultaneous Detection of Multiple Change Points and Community Structures in Time Series of Networks
Author(s): Alexander Aue*
Companies: University of California Davis
Keywords: Time Series; Networks; Community detection; Change-points

In many complex systems, networks and graphs arise in a natural manner. Often, time-evolving behavior can be easily found and modeled using time-series methodology. In network analysis, research problems can be largely divided into two categories: community detection and change-point detection. Community detection aims at finding specific sub-structures within the networks, and change-point detection tries to find the time points at which sub-structures change. We propose a novel methodology to detect both community structures and change-points simultaneously based on a model selection framework in which the Minimum Description Length Principle (MDL) is utilized as minimizing objective criterion. The promising practical performance of the proposed method is illustrated via a series of numerical experiments and real data analyses.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2021 program