Change Point Detection on A Separable Model for Dynamic Networks

Yik Lun Kei, Hangjian Li, Yanzhen Chen, OSCAR HERNAN MADRID PADILLA

Abstract: This paper studies the unsupervised change point detection problem in time series of networks using the Separable Temporal Exponential-family Random Graph Model (STERGM). Inherently, dynamic network patterns are complex due to dyadic and temporal dependence, and change points detection can identify the discrepancies in the underlying data generating processes to facilitate downstream analysis. In particular, the STERGM that utilizes network statistics and nodal attributes to represent the structural patterns is a flexible and parsimonious model to fit dynamic networks. We propose a new estimator derived from the Alternating Direction Method of Multipliers (ADMM) procedure and Group Fused Lasso (GFL) regularization to simultaneously detect multiple time points where the parameters of a time-heterogeneous STERGM have shifted. Experiments on both simulated and real data show good performance of the proposed framework, and an R package CPDstergm is developed to implement the method.

Submission Length: Long submission (more than 12 pages of main content)

Video: https://youtu.be/glTg6F7Pxhc?si=9Hj7BfTrOT_BgyW9

Code: https://github.com/allenkei/CPDstergm_demo

Assigned Action Editor: ~Yan_Liu1

Submission Number: 4269