graphBIX

Graph clustering by Bayesian inference with cross-validation model assessment.

This is free software, you can redistribute it and/or modify it under the terms of the GNU General Public License, version 3 or above. See LICENSE.txt for details.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

• sbm.jl Bayesian inference of the stochastic block model (with full degrees of freedom) using EM algorithm + belief propagation with the leave-one-out cross-validation.
• mod.jl Bayesian inference of the stochastic block model restricted to community structure using EM algorithm + belief propagation with the leave-one-out cross-validation.

USAGE

sbm.jl, mod.jl

To start, the following package needs to be imported:

using DocOpt
using PyPlot

For a given edgelist file, e.g. edgelist.txt,

julia sbm.jl edgelist.txt

generates the following outputs:

• Summary of model assessments (summary.txt)
  Input parameters / actual number of clusters & the number of iteration until convergence for each q.
• Detailed results of model assessments (assessment.txt):
  Values of the cluster sizes and the affinity matrices learned.
• Cluster assignments (assignment.txt):
  (i,q)-element indicates the cluster assignments of vertex i with the input number of clusters q.
• Plot of model assessments (assessment_"dataset".pdf)
• [optional] .smap files for the alluvial diagram

OPTIONS

julia sbm.jl -help

shows the options and more details.

REFERENCE

sbm.jl: Tatsuro Kawamoto and Yoshiyuki Kabashima, "Cross-validation estimate of the number of clusters in a network", Scientific Reports, 7, 3327 (2017).

mod.jl: Tatsuro Kawamoto and Yoshiyuki Kabashima, "Comparative analysis on the selection of number of clusters in community detection", arXiv:1606.07668 (2017).

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Author: Tatsuro Kawamoto: [email protected]