Online Program Home
My Program

Abstract Details

Activity Number: 132 - Statistical Analysis for Networks
Type: Contributed
Date/Time: Monday, July 30, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #328313 Presentation
Title: Segmenting Dynamic Network Data
Author(s): Rex Cheung*
Companies: San Francisco State University
Keywords: Change Point Detection; Dynamic Networks; Community Detection;

Networks and graphs arise naturally in many complex systems. Often times they exhibit a dynamic behavior that can be modeled using dynamic networks. Two major research problems in dynamic networks are 1) community detection, which aims to find specific sub-structures within the networks, and 2) change point detection, which tries to find the time points that the sub-structures change. This project proposes a new methodology to solve both problems simultaneously, by casting this as a model selection problem and utilizing the Minimum Description Length Principle (MDL) as the minimizing objective criterion. The derived detection algorithm is compatible with many existing methods, and is supported by empirical results and data analysis.

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

Back to the full JSM 2018 program