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 23 articles , 1 standard article )

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  1. Rombach, Puck; Porter, Mason A.; Fowler, James H.; Mucha, Peter J.: Core-periphery structure in networks (revisited) (2017)
  2. Taylor, Dane; Myers, Sean A.; Clauset, Aaron; Porter, Mason A.; Mucha, Peter J.: Eigenvector-based centrality measures for temporal networks (2017)
  3. Bazzi, Marya; Porter, Mason A.; Williams, Stacy; McDonald, Mark; Fenn, Daniel J.; Howison, Sam D.: Community detection in temporal multilayer networks, with an application to correlation networks (2016)
  4. Bertozzi, Andrea L.; Flenner, Arjuna: Diffuse interface models on graphs for classification of high dimensional data (2016)
  5. Burbano Lombana, Daniel Alberto; di Bernardo, Mario: Multiplex PI control for consensus in networks of heterogeneous linear agents (2016)
  6. Froyland, Gary; Kwok, Eric: Partitions of networks that are robust to vertex permutation dynamics (2015)
  7. Fu, Julei; Sun, Duoyong; Chai, Jian; Xiao, Jin; Wang, Shouyang: The “six-element” analysis method for the research on the characteristics of terrorist activities (2015)
  8. Fu, Julei; Fan, Ying; Wang, Yang; Wang, Shouyang: Network analysis of terrorist activities (2014)
  9. Wilson, James D.; Wang, Simi; Mucha, Peter J.; Bhamidi, Shankar; Nobel, Andrew B.: A testing based extraction algorithm for identifying significant communities in networks (2014)
  10. Xu, Kevin S.; Kliger, Mark; Hero, Alfred O.III: Adaptive evolutionary clustering (2014)
  11. Zhao, Dawei; Li, Lixiang; Peng, Haipeng; Luo, Qun; Yang, Yixian: Multiple routes transmitted epidemics on multiplex networks (2014)
  12. Berlingerio, Michele; Pinelli, Fabio; Calabrese, Francesco: ABACUS: frequent pattern mining-based community discovery in multidimensional networks (2013)
  13. Grindrod, Peter; Higham, Desmond J.: A matrix iteration for dynamic network summaries (2013)
  14. Hu, Huiyi; Laurent, Thomas; Porter, Mason A.; Bertozzi, Andrea L.: A method based on total variation for network modularity optimization using the MBO scheme (2013)
  15. Li, Wenye: Revealing network communities with a nonlinear programming method (2013)
  16. Simpson, Sean L.; DuBois Bowman, F.; Laurienti, Paul J.: Analyzing complex functional brain networks: fusing statistics and network science to understand the brain (2013)
  17. Solá, Luis; Romance, Miguel; Criado, Regino; Flores, Julio; García del Amo, Alejandro; Boccaletti, Stefano: Eigenvector centrality of nodes in multiplex networks (2013)
  18. van Gennip, Yves; Hunter, Blake; Ahn, Raymond; Elliott, Peter; Luh, Kyle; Halvorson, Megan; Reid, Shannon; Valasik, Matthew; Wo, James; Tita, George E.; Bertozzi, Andrea L.; Brantingham, P.Jeffrey: Community detection using spectral clustering on sparse geosocial data (2013)
  19. Xu, Kevin S.; Kliger, Mark; Hero, Alfred O. III: A regularized graph layout framework for dynamic network visualization (2013)
  20. Mutlu, Ali Yener; Bernat, Edward; Aviyente, Selin: A signal-processing-based approach to time-varying graph analysis for dynamic brain network identification (2012)

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