KAP-CPD: Kernel Aggregation for Change-Point Detection in Dynamic Networks

TL;DR

KAP-CPD is a flexible, kernel-aggregation framework for change-point detection in dynamic networks that adapts to various change patterns without assuming specific distributions.

Contribution

It introduces a novel kernel aggregation approach for change-point detection that enhances adaptability and computational efficiency in dynamic network analysis.

Findings

Achieves strong empirical power across diverse change scenarios.

Outperforms existing methods in computational speed for long sequences.

Effectively detects change points in real-world email and brain networks.

Abstract

Change-point detection in dynamic networks has received much attention due to its broad applications in social networks and biological systems. Kernel-based methods have shown strong potential for this problem. However, their performance can depend sensitively on the choice of kernel, and selecting an appropriate kernel is challenging when the underlying change pattern is unknown. Motivated by this challenge, we propose KAP-CPD, a new kernel-based testing framework for change-point detection in dynamic networks. KAP-CPD aggregates information from multiple kernels, allowing it to adapt to diverse change patterns. The proposed method does not assume specific underlying network distribution, and achieves strong empirical power across a wide range of network change scenarios. To improve scalability, we further develop a fast analytic testing procedure, KAPf-CPD, that substantially reduces computation time for long network sequences compared with permutation-based alternatives and current state-of-the-art methods. We evaluate our proposed framework through extensive simulations and real-world data on email communication networks and brain functional connectivity networks.