https://github.com/tatsuro-kawamoto/graphBIX

Graph clustering by Bayesian inference with cross-validation model assessment
https://github.com/tatsuro-kawamoto/graphBIX

Last synced: 25 days ago
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Graph clustering by Bayesian inference with cross-validation model assessment

• Host: GitHub
• URL: https://github.com/tatsuro-kawamoto/graphBIX
• Owner: tatsuro-kawamoto
• License: gpl-3.0
• Created: 2016-04-18T14:25:36.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2019-05-29T11:43:18.000Z (about 5 years ago)
• Last Synced: 2024-02-24T14:31:23.243Z (4 months ago)
• Language: Jupyter Notebook
• Size: 76.2 KB
• Stars: 2
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

Lists

• community-detection-awesome-provided - [Julia Reference
• awesome-community-detection - [Julia Reference

README

        
# 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]