MODE-NET: MOdules DEtection in NETworks Image
    This page contains codes, papers and data on the algorithms we have been developing using Belief Propagation and spectral methods for identifiying functional modules in networks, such as community detection problems.



    A c++ implementation of our Belief Propagation algorithm

    Please feel free to download the c implementation of our BP approach coded by Aurelien Decelle, Florent Krzakala and Pan Zhang. We would be more than happy to receive comments and suggestions. br>


    A matlab implementation for clustering with the Bethe Hessian

    Please feel free to download the matlab implementation of our spectral clustering method coded by Alaa Saade. Again We would be more than happy to receive comments and suggestions.


    Related articles

    The Belief Propagation approach has been described in the two following papers by Aurelien Decelle, Florent Krzakala, Cristopher Moore and Lenka Zdeborova:
    * Phase transition in the detection of modules in sparse networks     in Phys. Rev. Lett. 107, 065701 (2011)
    * Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications in Phys. Rev. E 84, 066106 (2011).

    The spectral approach follows the recent paper by Alaa Saade, Florent Krzakala and Lenka Zdeborova:
    * Spectral Clustering of Graphs with the Bethe Hessian :
    and is based of a breakthrought paper published earlier:
    * Spectral redemption: clustering sparse networks by Florent Krzakala, Cristopher Moore, Elchanan Mossel, Joe Neeman, Allan Sly, Lenka Zdeborova, Pan Zhang, published in the proceedings of the national academy of sciences in 2012.


    This is mode_net.krzakala.org.
    This page is maintained by Florent Krzakala

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