sarielma/markov_clustering

markov clustering in python

Markov Clustering

This module implements of the MCL algorithm in python.

The MCL algorithm was developed by Stijn van Dongen at the University of Utrecht.

Details of the algorithm can be found on the MCL homepage.

Features

• Sparse matrix support
• Pruning

Requirements

• Core requirements

  • Python 3.x
  • numpy
  • scipy
  • scikit-learn

• Optional (required for visualization)

  • networkx
  • matplotlib

• To run the tests

  • pytest

Installation

The recommended installation method is via pip.

To install with all requirements including support for visualization:

pip install markov_clustering[drawing]

To install with only support for the core MCL clustering:

pip install markov_clustering

Example

We will use NetworkX to generate the adjacency matrix for a random geometric graph which contains 200 nodes with random coordinates ranging from (-1,-1) to (1,1). Nodes are considered adjacent if the distance between them is <= 0.3 units.

This example assumes that the optional dependencies (matplotlib and networkx) have been installed

import markov_clustering as mc
import networkx as nx
import random

# number of nodes to use
numnodes = 200

# generate random positions as a dictionary where the key is the node id and the value
# is a tuple containing 2D coordinates
positions = {i:(random.random() * 2 - 1, random.random() * 2 - 1) for i in range(numnodes)}

# use networkx to generate the graph
network = nx.random_geometric_graph(numnodes, 0.3, pos=positions)

# then get the adjacency matrix (in sparse form)
matrix = nx.to_scipy_sparse_matrix(network)

We can then run the MCL algorithm on the adjacency matrix and retrieve the clusters.

result = mc.run_mcl(matrix)           # run MCL with default parameters
clusters = mc.get_clusters(result)    # get clusters

Finally, we can draw the results. The draw_graph function only requires the adjacency matrix and the cluster list, but we will pass some extra parameters such as the node positions, set the node size, disable labels and set the color for edges.

mc.draw_graph(matrix, clusters, pos=positions, node_size=50, with_labels=False, edge_color="silver")

This should result in an image similar to the one at the top of this section.

If the clustering is too fine for your taste, reducing the MCL inflation parameter to 1.4 (from the default of 2) will result in coarser clustering. e.g.

result = mc.run_mcl(matrix, inflation=1.4)
clusters = mc.get_clusters(result)
mc.draw_graph(matrix, clusters, pos=positions, node_size=50, with_labels=False, edge_color="silver")