Mosaic Benchmark Networks: Modular Link Streams for Testing Dynamic Community Detection Algorithms

Mosaic Benchmark Networks: Modular Link Streams for Testing Dynamic Community Detection Algorithms. Asgari, Y., Cazabet, R., & Borgnat, P. In Complex Networks & Their Applications XII, pages 209–222, Cham, 2024. Springer Nature Switzerland.

ArXiv doi abstract bibtex

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.

@inproceedings{asgariMosaicBenchmarkNetworks2024,
  title = {Mosaic {{Benchmark Networks}}: {{Modular Link Streams}} for~{{Testing Dynamic Community Detection Algorithms}}},
  shorttitle = {Mosaic {{Benchmark Networks}}},
  booktitle = {Complex {{Networks}} \& {{Their Applications XII}}},
  author = {Asgari, Yasaman and Cazabet, Remy and Borgnat, Pierre},
  year = 2024,
  pages = {209--222},
  publisher = {Springer Nature Switzerland},
  address = {Cham},
  doi = {10.1007/978-3-031-53499-7_17},
  url = {arXiv=https://arxiv.org/abs/2310.02840},
  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.},
  isbn = {978-3-031-53499-7},
  langid = {english},
  keywords = {community detection,network science,temporal networks}
}

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