Skip to content

Latest commit

 

History

History
executable file
·
57 lines (42 loc) · 2.14 KB

README.md

File metadata and controls

executable file
·
57 lines (42 loc) · 2.14 KB
Raw

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", Phys. Rev. E 97, 022315 (2018).

labeled_sbm: Tatsuro Kawamoto, "Algorithmic detectability threshold of the stochastic block model", Phys. Rev. E 97, 032301 (2018).

============
Author: Tatsuro Kawamoto: [email protected]