# Detecting Stable Communities in Link Streams at Multiple Temporal Scales

## TL;DR

This paper introduces a novel method for detecting stable communities in link streams across multiple temporal scales, effectively identifying meaningful community structures and change points in evolving networks.

## Contribution

The proposed approach efficiently discovers stable communities at various temporal scales, improving upon existing dynamic community detection algorithms.

## Key findings

- Successfully detects stable communities in synthetic networks
- Effectively identifies community structures in real social contact networks
- Operates efficiently across multiple temporal scales

## Abstract

Link streams model interactions over time in a wide range of fields. Under this model, the challenge is to mine efficiently both temporal and topological structures. Community detection and change point detection are one of the most powerful tools to analyze such evolving interactions. In this paper, we build on both to detect stable community structures by identifying change points within meaningful communities. Unlike existing dynamic community detection algorithms, the proposed method is able to discover stable communities efficiently at multiple temporal scales. We test the effectiveness of our method on synthetic networks, and on high-resolution time-varying networks of contacts drawn from real social networks.