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 32 articles , 1 standard article )

Showing results 1 to 20 of 32.
Sorted by year (citations)

1 2 next

  1. Bredereck, Robert; Komusiewicz, Christian; Kratsch, Stefan; Molter, Hendrik; Niedermeier, Rolf; Sorge, Manuel: Assessing the computational complexity of multi-layer subgraph detection (2017)
  2. Masuda, Naoki; Porter, Mason A.; Lambiotte, Renaud: Random walks and diffusion on networks (2017)
  3. Rombach, Puck; Porter, Mason A.; Fowler, James H.; Mucha, Peter J.: Core-periphery structure in networks (revisited) (2017)
  4. Taylor, Dane; Myers, Sean A.; Clauset, Aaron; Porter, Mason A.; Mucha, Peter J.: Eigenvector-based centrality measures for temporal networks (2017)
  5. Zeng, An; Shen, Zhesi; Zhou, Jianlin; Wu, Jinshan; Fan, Ying; Wang, Yougui; Stanley, H.Eugene: The science of science: from the perspective of complex systems (2017)
  6. 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)
  7. Bertozzi, Andrea L.; Flenner, Arjuna: Diffuse interface models on graphs for classification of high dimensional data (2016)
  8. Burbano Lombana, Daniel Alberto; di Bernardo, Mario: Multiplex PI control for consensus in networks of heterogeneous linear agents (2016)
  9. Cucuringu, Mihai; Rombach, Puck; Lee, Sang Hoon; Porter, Mason A.: Detection of core-periphery structure in networks using spectral methods and geodesic paths (2016)
  10. Juang, Jonq; Liang, Yu-Hao: The impact of vaccine success and awareness on epidemic dynamics (2016)
  11. Shekhtman, Louis M.; Danziger, Michael M.; Havlin, Shlomo: Recent advances on failure and recovery in networks of networks (2016)
  12. Wehmuth, Klaus; Fleury, Éric; Ziviani, Artur: On multiaspect graphs (2016)
  13. Froyland, Gary; Kwok, Eric: Partitions of networks that are robust to vertex permutation dynamics (2015)
  14. 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)
  15. Hmimida, Manel; Kanawati, Rushed: Community detection in multiplex networks: a seed-centric approach (2015)
  16. Fu, Julei; Fan, Ying; Wang, Yang; Wang, Shouyang: Network analysis of terrorist activities (2014)
  17. Luo, Chao; Wang, Xingyuan; Liu, Hong: Controllability of asynchronous Boolean multiplex control networks (2014)
  18. 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)
  19. Xu, Kevin S.; Kliger, Mark; Hero, Alfred O.III: Adaptive evolutionary clustering (2014)
  20. Zhao, Dawei; Li, Lixiang; Peng, Haipeng; Luo, Qun; Yang, Yixian: Multiple routes transmitted epidemics on multiplex networks (2014)

1 2 next