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    Mixing local and global information for community detection in large networks

    De Meo, P. and Ferrara, E. and Fiumara, G. and Provetti, Alessandro (2014) Mixing local and global information for community detection in large networks. Journal of Computer and System Sciences 80 (1), pp. 72-87. ISSN 0022-0000.

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    Clustering networks play a key role in many scientific fields, from Biology to Sociology and Computer Science. Some clustering approaches are called global because they exploit knowledge about the whole network topology. Vice versa, so-called local methods require only a partial knowledge of the network topology. Global approaches yield accurate results but do not scale well on large networks; local approaches, vice versa, are less accurate but computationally fast. We propose CONCLUDE (COmplex Network CLUster DEtection), a new clustering method that couples the accuracy of global approaches with the scalability of local methods. CONCLUDE generates random, non-backtracking walks of finite length to compute the importance of each edge in keeping the network connected, i.e., its edge centrality. Edge centralities allow for mapping vertices onto points of a Euclidean space and compute all-pairs distances between vertices; those distances are then used to partition the network into clusters.


    Item Type: Article
    Keyword(s) / Subject(s): Complex networks, Community detection, Social networks, Social network analysis
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Alessandro Provetti
    Date Deposited: 12 Oct 2018 13:04
    Last Modified: 09 Aug 2023 12:45


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