https://github.com/ntamas/blockmodel

Fitting stochastic blockmodels to graphs
https://github.com/ntamas/blockmodel

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Fitting stochastic blockmodels to graphs

• Host: GitHub
• URL: https://github.com/ntamas/blockmodel
• Owner: ntamas
• Created: 2010-12-06T17:14:10.000Z (almost 14 years ago)
• Default Branch: master
• Last Pushed: 2016-07-08T10:36:42.000Z (over 8 years ago)
• Last Synced: 2023-03-10T23:12:53.548Z (over 1 year ago)
• Language: C++
• Homepage:
• Size: 230 KB
• Stars: 16
• Watchers: 3
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst

Awesome Lists containing this project

README

        
==========

blockmodel

==========

----------------------------------------------------

Fitting stochastic blockmodels to empirical networks

----------------------------------------------------

:Author: Tamas Nepusz

:Version: 0.1

:License: GPL

This repository contains ``block-fit``, ``block-gen`` and ``block-pred``,

a suite of three programs to work with stochastic blockmodels (both

degree-corrected and standard ones). ``block-fit`` fits a standard or

degree-corrected blockmodel to a given graph, ``block-gen`` generates graphs

from a fitted model and ``block-pred`` calculates the probability of existence

for each possible edge in a grpah from a fitted stochastic blockmodel. More

details about the usage of each program are to be found in the ``doc``

subfolder. Please also read the references [1]_ [2]_ [3]_ [4]_ if you are

interested in how these models work.

Precompiled binaries

====================

Sorry, we do not provide precompiled binaries yet - you have to compile the

tools on your own.

Compiling from source code

==========================

Requirements

------------

- igraph_ 0.7.1 or later. This is the library that we use to work with graphs.

- CMake_ to generate the makefiles (or the project file if you are using

  Visual Studio).

.. _igraph: http://igraph.org

.. _CMake: http://www.cmake.org

Compiling using ``cmake`` and ``make``

--------------------------------------

These instructions are for Linux or Mac OS X and assume that igraph_ is

installed in a way that CMake can figure out automatically where it is.

(This usually involves using ``pkg-config``; if you run ``pkg-config --cflags igraph``

and it works, then it should work with CMake as well)::

    $ git submodule update --init

    $ mkdir build

    $ cd build

    $ cmake ..

    $ make

The first command is required only after you have checked out the source code

from GitHub for the first time. The command fetches the source code of the

C++ interface of igraph_ from GitHub and adds it to the source tree.

Bugs, questions?

================

Have you found a bug in the code? Do you have questions? Let me know.

I think you are smart enough to figure out my email address by googling

for my name. Or just drop me a message on GitHub.

References

==========

.. [1] Snijders TAB, Nowicki K (1997) Estimation and prediction for stochastic

       blockmodels for graphs with latent block structure. *J Classif*

       **14**:75-100.

.. [2] Nepusz T, Négyessy L, Tusnády G, Bazsó F (2008) Reconstructing cortical

       networks: case of directed graphs with high level of reciprocity. In:

       Bollobás B, Kozma R, Miklós D, editors, Handbook of Large-Scale Random

       Networks, Springer, volume 18 of *Bolyai Society Mathematical Studies*,

       pp. 325-368.

.. [3] Karrer B, Newman MEJ (2011) Stochastic blockmodels and community

       structure in networks. *Phys Rev E* **83**:016107.

.. [4] Nepusz T, Paccanaro A (2013) De-noising protein-protein interaction

       networks with random graph models. In preparation.