# Simultaneous Detection of Multiple Change Points and Community Structures in Time Series of Networks

## TL;DR

This paper introduces a new method for simultaneously detecting change points and community structures in evolving network time series using a model selection approach based on the MDL principle, demonstrated through experiments.

## Contribution

It presents a novel methodology that jointly detects change points and community structures in network time series, leveraging the MDL principle for model selection.

## Key findings

- Effective in identifying change points and communities
- Performs well on real network data
- Outperforms existing separate detection methods

## Abstract

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. Amongst others, two common research problems in network analysis are 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 analysis.