  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.
