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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.