This project implements a community detection algorithm using divisive hierarchical clustering (Girvan-Newman algorithm!).
It makes use of 2 python libraries called networkx and community. The project uses betweenness function and the modularity function which are a part of the networkx and the community libraries respectively. It also uses the matplotlib library for plotting the communities.
The python code takes two parameters, namely input file containing the graph and the output image with the community structure.
For example:
python detect_community.py input.txt image.png
#Input Parameters:
input.txt: This file consists of the representation of the graph. All graphs 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 this algorithm. Each community is represented in a unique color. Each node contains a label representing the node numbers in the input file.
#Output: The Python code outputs the communities in the form of arrays to standard output (the console). Each community is an array representing nodes in that community.
For example:
[1,2,3]
[4,5,6]
[7]
[8]
[9,10,11]
[12,13,14]
These 6 arrays represent the 6 communities in the input graph.
Also, the community graph will be stored in output file.
Sample graph for above community:
