Abstract
Community detection in networks is an important problem with applications in various scientific fields. Most state-of-the-art methods rely on the assumption that the algorithm will be executed in a sequential manner. As the size of networks under study grows, faster methods must be developed in order to obtain results in reasonable amounts of time. In this work, we extend parallel community detection methods based on current fast state-of-the-art algorithms to exploit the sparse structure of real networks, and evaluate their effectiveness.
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Gagnon, P., Caporossi, G., Perron, S. (2018). Parallel Community Detection Methods for Sparse Complex Networks. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_24
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DOI: https://doi.org/10.1007/978-3-319-72150-7_24
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