This module implements the Markov Cluster algorithm created by Stijn van Dongen and described in http://www.library.uu.nl/digiarchief/dip/diss/1895620/inhoud.htm.
This implementation is still in beta and is not yet optimized for large networks.
![example] (example.png)
##Installation
python setup.py install
##Usage
###Command line
Usage: mcl_clustering.py [options] <input_matrix>
Options
-h, --help show this help message and exit
-e EXPAND_FACTOR, --expand_factor=EXPAND_FACTOR
expand factor (default: 2)
-i INFLATE_FACTOR, --inflate_factor=INFLATE_FACTOR
inflate factor (default: 2)
-m MULT_FACTOR, --mult_factor=MULT_FACTOR
multiply factor (default: 2)
-l MAX_LOOP, --max_loops=MAX_LOOP
max loops (default: 60)
-o FILE, --output=FILE
output (default: stdout)
-v, --verbose verbose (default: True)
-d, --draw-graph show graph with networkx (default: False)
The input matrix must be as comma-separated value file representing the adjacency matrix.
An example is the file example.csv in examples dir.
try: mcl_clustering.py -d -e 3 <example_path>/example.csv
###Code
####Using numpy adjacency matrix
from mcl_clustering import mcl
A = <your matrix>
M, clusters = mcl(A, expand_factor = <expand_factor>,
inflate_factor = <inflate_factor>,
max_loop = <max_loop>,
mult_factor = <mult_factor>)
####Using a networkx graph
from mcl_clustering import networkx_mcl
G = <your graph>
M, clusters = networkx_mcl(G, expand_factor = <expand_factor>,
inflate_factor = <inflate_factor>,
max_loop = <max_loop>,
mult_factor = <mult_factor>)
Output
M = otuput matrix
clusters = dict with keys = [<cluster id>] values = [<vertex id>]
##Requirements
numpy
networkx
##References
-
[1] Stijn van Dongen, Graph Clustering by Flow Simulation. PhD thesis, University of Utrecht, May 2000. ( http://www.library.uu.nl/digiarchief/dip/diss/1895620/inhoud.htm )
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[2] Stijn van Dongen. A cluster algorithm for graphs. Technical Report INS-R0010, National Research Institute for Mathematics and Computer Science in the Netherlands, Amsterdam, May 2000. ( http://www.cwi.nl/ftp/CWIreports/INS/INS-R0010.ps.Z )
MIT