| 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.
Sorry, we do not provide precompiled binaries yet - you have to compile the tools on your own.
- 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).
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.
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.
| [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. |