random-modular-network-generator

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random-modular-network-generator

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Generates random graphs with tunable strength of community structure

random-modular-network-generator 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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Random Modular Network Generator
================================
This is the source code used for the following paper:

Sah, Pratha*, Lisa O. Singh, Aaron Clauset, and Shweta Bansal. "Exploring community structure in biological networks with random graphs." BMC Bioinformatics 2014, 15:220. doi:10.1186/1471-2105-15-220

This Python script generates undirected, simple, connected graphs with a specified degrees and pattern of communities, while maintaining a graph structure that is as random as possible.

email: ps875@georgetown.edu, sb753@georgetown.edu

Please cite the paper above, if you use our code in any form or create a derivative work.

Sample Output Graph
================================

![alt tag](https://github.com/prathasah/random-modular-network-generator/blob/master/Figure2.jpg)

Modular random graphs with network size = 150, 375 edges, 3 modules (or communities) of size, *s* = 50 and degree distribution is power law with modularity values of: a) *Q* = 0.1; b) *Q* = 0.3; and c) *Q* = 0.6.

From  Figure 2, Sah *et al.* (2014)

Dependencies
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* [Python 2.7](http://python.org/)
* [Networkx 2.2](https://networkx.github.io/)

Link to install Networkx can be found [here](https://networkx.github.io/). The generator also requires sequence_generator.py script which is provided.

Usage
================================

For a quick demo of the code, open the script "random_modular_generator_variable_modules.py" and scroll down to the *main* function. Adjust the model parameters, degree distribution function and module size distribution function and run in terminal using the command:

`$ python random_modular_generator_variable_modules.py`

Sample Code
================================
For users unfamiliar to Python, I have uploadeded a sample code file (mock_code.py) demonstrating how the graph generator can be imported and used in a script. The mock code can be run using the command

`$ python mock_code.py`

Output
================================
The code saves the the adjacency matrix of the generated random modular graph under the filename 'adjacency_matrix.txt'. The graph is also saved in a graphml format under the filename "random_modular_graph.graphml". The graphml file can be uploaded in Gephi (http://gephi.github.io/) for graph visualization. Note that Gephi currently does not have a layout plugin to visualize modular graphs.  In the near future, I am planning to add a function in random-modular-network-generator.py code to assign each node a particular coordinate that allows easy visualization of modules (I have done this in Figure 2). Shoot us an email if you need this feature sooner than later.

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