A Generalized Louvain Method for Community Detection Implemented in MATLAB. This ”generalized Louvain” MATLAB code for community detection allows the user to define a quality function in terms of a generalized-modularity null model framework and then follows a two-phase iterative procedure similar to the ”Louvain” method, with the important distinction that the Louvain passes in the codes here work directly with the modularity matrix, not the adjacency matrix. That is, the main genlouvain.m code can be used with any quality function specified in terms of a modularity matrix; but as such it does not take advantage of any particular structure to those matrices (cf. the excellent findcommunities code).

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  1. Liang, Bo; Wang, Lin; Wang, Xiaofan: OLMNE+FT: multiplex network embedding based on overlapping links (2022)
  2. Ma, Weicai; Zhang, Peng; Zhao, Xin; Xue, Leyang: The coupled dynamics of information dissemination and SEIR-based epidemic spreading in multiplex networks (2022)
  3. Ning, Di; Chen, Juan; Jiang, Meiying: Pinning impulsive synchronization of two-layer heterogeneous delayed networks (2022)
  4. Paul, Subhadeep; Chen, Yuguo: Null models and community detection in multi-layer networks (2022)
  5. Atzmueller, Martin; Günnemann, Stephan; Zimmermann, Albrecht: Mining communities and their descriptions on attributed graphs: a survey (2021)
  6. Bergermann, Kai; Stoll, Martin; Volkmer, Toni: Semi-supervised learning for aggregated multilayer graphs using diffuse interface methods and fast matrix-vector products (2021)
  7. Berner, Rico; Mehrmann, Volker; Schöll, Eckehard; Yanchuk, Serhiy: The multiplex decomposition: an analytic framework for multilayer dynamical networks (2021)
  8. Huang, Xinyu; Chen, Dongming; Ren, Tao; Wang, Dongqi: A survey of community detection methods in multilayer networks (2021)
  9. Li, Juyi; Luan, Yangyang; Wu, Xiaoqun; Lu, Jun-an: Synchronizability of double-layer dumbbell networks (2021)
  10. Mata, Angélica S.; Dourado, Stela M. P.: Mathematical modeling applied to epidemics: an overview (2021)
  11. Shafie, Termeh; Schoch, David: Multiplexity analysis of networks using multigraph representations (2021)
  12. Taylor, Dane; Porter, Mason A.; Mucha, Peter J.: Tunable eigenvector-based centralities for multiplex and temporal networks (2021)
  13. Torres, Leo; Blevins, Ann S.; Bassett, Danielle; Eliassi-Rad, Tina: The why, how, and when of representations for complex systems (2021)
  14. Yan, Huibiao; Zhou, Jin; Li, Weiqiang; Lu, Jun-an; Fan, Ruguo: Superdiffusion criteria on duplex networks (2021)
  15. Zhong, Linfeng; Xue, Xiaoyu; Bai, Yu; Huang, Jin; Cheng, Qing; Huang, Longyang; Pan, Weijun: Information spreading on activity-driven temporal networks with two-step memory (2021)
  16. Bedru, Hayat Dino; Yu, Shuo; Xiao, Xinru; Zhang, Da; Wan, Liangtian; Guo, He; Xia, Feng: Big networks: a survey (2020)
  17. Hsu, Bay-Yuan; Tu, Chia-Lin; Chang, Ming-Yi; Shen, Chih-Ya: CrawISN: community-aware data acquisition with maximum willingness in online social networks (2020)
  18. Meeks, Kitty; Skerman, Fiona: The parameterised complexity of computing the maximum modularity of a graph (2020)
  19. Mihm, Maximilian; Toth, Russell: Cooperative networks with robust private monitoring (2020)
  20. Paul, Subhadeep; Chen, Yuguo: Spectral and matrix factorization methods for consistent community detection in multi-layer networks (2020)

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