Doubly stochastic scaling unifies community detection
le Gorrec, Luce and Mouysset, Sandrine and Ruiz, Daniel (2022) Doubly stochastic scaling unifies community detection. Neurocomputing, 504. pp. 141-162. ISSN 0925-2312 (https://doi.org/10.1016/j.neucom.2022.06.090)
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Abstract
Graph partitioning, or community detection, has been widely investigated in network science. Yet, the correct community structure on a given network is essentially data-driven. Thus, instead of a formal definition, diverse measures have been conceived to capture intuitive desirable properties shared by most of the community structures. In this work, we propose a preprocessing based on a doubly stochastic scaling of network adjacency matrices, to highlight these desirable properties. By investigating a range of community detection measures, and carefully generalising them to doubly stochastic graphs, we show that such a scaling unifies a whole category of these measures—namely, the so-called linear criteria—onto two unique measures to set up. Finally, to help practitioners setting up these measures, we provide an extensive numerical comparison of the capacity of these measures to uncover community structures within stochastic block models, using the Louvain algorithm.
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Item type: Article ID code: 81402 Dates: DateEvent14 September 2022Published3 July 2022Published Online27 June 2022AcceptedSubjects: Science > Mathematics > Electronic computers. Computer science Department: Faculty of Science > Mathematics and Statistics Depositing user: Pure Administrator Date deposited: 07 Jul 2022 13:59 Last modified: 02 Sep 2024 01:01 URI: https://strathprints.strath.ac.uk/id/eprint/81402