Community-Detection

Implement a community detection algorithm using a divisive hierarchical clustering (Girvan-Newman algorithm)

Overview

Implement a community detection algorithm using a divisive hierarchical clustering (Girvan-Newman algorithm). It will make use of 2 python libraries called networkx and community. The networkx is a python library which can be installed on your machines. The assignment will require making use of the betweenness function and the modularity function which are a part of the networkx and the community libraries respectively. The matplotlib library will be used for plotting the communities.

Main Steps

• Read the input file into a graph using Networkx.
• Use the betweenness of the edges as a measure to break communities into smaller
• communities in divisive clustering.
• The result should be the set of communities that have the highest modularity.
• Use the modularity function in the community API
• Use the betweenness function from networkx
• After the best set of communities are obtained, use matplotlib and networkx functions to plot the communities with different colors.

Execution Details

The python code should take in two parameters, namely input file containing the graph and the output image with the community structure. For example: Python communities.py input.txt image.png

Input Parameters

input.txt

This file consists of the representation of the graph. All graphs tested will be undirected graphs. Each line in the input file is of the format: 1 2 where 1 and 2 are the nodes and each line represents an edge between the two nodes. The nodes are separated by one space.

image.png

This will be a visualization of the communities detected by your algorithm. You should represent each communities in a unique color. Each node should contain a label representing the node numbers in the input file.

Output

The Python code should output the communities in the form of a dictionary to standard output (the console). Each community should be an array representing nodes in that community. In each array, the nodes should be sorted lexicographically. For example:

    [1,2,3,4] 
    [5,6,7,8] 
    [9]
    [10]

These 4 arrays represent the 4 communities.