Mosaic benchmark networks: Modular link streams for testing dynamic community detection algorithms

TL;DR

This paper introduces a new framework for generating synthetic modular link streams with predefined communities, enabling better evaluation of dynamic community detection algorithms on highly detailed temporal networks.

Contribution

The paper presents a novel framework for creating synthetic link streams with known community structures, addressing the gap in benchmarking dynamic community detection methods.

Findings

Different algorithms show varying performance on the new benchmark.

No single method consistently outperforms others across all scenarios.

The framework reveals limitations of existing algorithms not evident in snapshot-based evaluations.

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.