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      From Louvain to Leiden: guaranteeing well-connected communities

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      Scientific Reports
      Springer Science and Business Media LLC

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          Abstract

          Community detection is often used to understand the structure of large and complex networks. One of the most popular algorithms for uncovering community structure is the so-called Louvain algorithm. We show that this algorithm has a major defect that largely went unnoticed until now: the Louvain algorithm may yield arbitrarily badly connected communities. In the worst case, communities may even be disconnected, especially when running the algorithm iteratively. In our experimental analysis, we observe that up to 25% of the communities are badly connected and up to 16% are disconnected. To address this problem, we introduce the Leiden algorithm. We prove that the Leiden algorithm yields communities that are guaranteed to be connected. In addition, we prove that, when the Leiden algorithm is applied iteratively, it converges to a partition in which all subsets of all communities are locally optimally assigned. Furthermore, by relying on a fast local move approach, the Leiden algorithm runs faster than the Louvain algorithm. We demonstrate the performance of the Leiden algorithm for several benchmark and real-world networks. We find that the Leiden algorithm is faster than the Louvain algorithm and uncovers better partitions, in addition to providing explicit guarantees.

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          Most cited references34

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          Fast unfolding of communities in large networks

          Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008
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            Complex brain networks: graph theoretical analysis of structural and functional systems.

            Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
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              Finding and evaluating community structure in networks

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                Author and article information

                Contributors
                Journal
                Scientific Reports
                Sci Rep
                Springer Science and Business Media LLC
                2045-2322
                December 2019
                March 26 2019
                December 2019
                : 9
                : 1
                Article
                10.1038/s41598-019-41695-z
                74af936b-8991-44ed-9847-602f5b365e93
                © 2019

                https://creativecommons.org/licenses/by/4.0

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