GenLouvain

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).


References in zbMATH (referenced in 84 articles , 1 standard article )

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  1. Taylor, Dane; Porter, Mason A.; Mucha, Peter J.: Tunable eigenvector-based centralities for multiplex and temporal networks (2021)
  2. 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)
  3. Bedru, Hayat Dino; Yu, Shuo; Xiao, Xinru; Zhang, Da; Wan, Liangtian; Guo, He; Xia, Feng: Big networks: a survey (2020)
  4. 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)
  5. Interdonato, Roberto; Magnani, Matteo; Perna, Diego; Tagarelli, Andrea; Vega, Davide: Multilayer network simplification: approaches, models and methods (2020)
  6. Meeks, Kitty; Skerman, Fiona: The parameterised complexity of computing the maximum modularity of a graph (2020)
  7. Mihm, Maximilian; Toth, Russell: Cooperative networks with robust private monitoring (2020)
  8. Paul, Subhadeep; Chen, Yuguo: Spectral and matrix factorization methods for consistent community detection in multi-layer networks (2020)
  9. Paul, Subhadeep; Chen, Yuguo: A random effects stochastic block model for joint community detection in multiple networks with applications to neuroimaging (2020)
  10. Rakshit, Sarbendu; Bera, Bidesh K.; Bollt, Erik M.; Ghosh, Dibakar: Intralayer synchronization in evolving multiplex hypernetworks: analytical approach (2020)
  11. Sun, Chengbin; Luo, Chao: Co-evolution of influence-based preferential selection and limited resource with multi-games on interdependent networks (2020)
  12. Vaiana, Michael; Muldoon, Sarah Feldt: Multilayer brain networks (2020)
  13. Wang, Dingjie; Yu, Wei; Zou, Xiufen: Tensor-based mathematical framework and new centralities for temporal multilayer networks (2020)
  14. Mezić, Igor; Fonoberov, Vladimir A.; Fonoberova, Maria; Sahai, Tuhin: Spectral complexity of directed graphs and application to structural decomposition (2019)
  15. Rasti, Saeid; Vogiatzis, Chrysafis: A survey of computational methods in protein-protein interaction networks (2019)
  16. Xia, Chengyi; Wang, Zhishuang; Zheng, Chunyuan; Guo, Quantong; Shi, Yongtang; Dehmer, Matthias; Chen, Zengqiang: A new coupled disease-awareness spreading model with mass media on multiplex networks (2019)
  17. Barnard, Rosanna C.; Kiss, Istvan Z.; Berthouze, Luc; Miller, Joel C.: Edge-based compartmental modelling of an SIR epidemic on a dual-layer static-dynamic multiplex network with tunable clustering (2018)
  18. Boyd, Zachary M.; Bae, Egil; Tai, Xue-Cheng; Bertozzi, Andrea L.: Simplified energy landscape for modularity using total variation (2018)
  19. Huang, Yu-Jhe; Juang, Jonq; Liang, Yu-Hao; Wang, Hsin-Yu: Global stability for epidemic models on multiplex networks (2018)
  20. Li, Zichao; Mucha, Peter J.; Taylor, Dane: Network-ensemble comparisons with stochastic rewiring and von Neumann entropy (2018)

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