Mosaic benchmark networks: Modular link streams for testing dynamic community detection algorithms
Year: 2023
Type: preprint
Abstract: Community structure is a critical feature of real networks, providing insights into nodes' internal organization. Nowadays, with the availability of highly detailed temporal networks such as link streams, studying community structures becomes more complex due to increased data precision and time sensitivity. Despite numerous algorithms developed in the past decade for dynamic community discovery, assessing their performance on link streams remains a challenge. Synthetic benchmark graphs are a well-accepted approach for evaluating static community detection algorithms. Additionally, there have been some proposals for slowly evolving communities in low-resolution temporal networks like snapshots. Nevertheless, this approach is not yet suitable for link streams. To bridge this gap, we introduce a novel framework that generates synthetic modular link streams with predefined communities. Subsequently, we evaluate established dynamic community detection methods to uncover limitations that may not be evident in snapshots with slowly evolving communities. While no method emerges as a clear winner, we observe notable differences among them.
Source: arXiv (Cornell University)
Institutions: École Normale Supérieure de Lyon, University of Zurich, Laboratoire de Physique de l'ENS de Lyon, Lyon 1 Université, Centre National de la Recherche Scientifique, Laboratoire d'Informatique en Images et Systèmes d'Information, Institut Rhônalpin des Systèmes Complexes, Institut National des Sciences Appliquées de Lyon
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4387390180