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Giuseppe Vettigli works at the Cybernetics Institute of the Italian National Reasearch Council. He is mainly focused on scientific software design and development. His main interests are in Artificial Intelligence, Data Mining and Multimedia applications. He is a Linux user and his favorite programming languages are Java and Python. You can check his blog about Python programming or follow him on Twitter. Giuseppe is a DZone MVB and is not an employee of DZone and has posted 34 posts at DZone. You can read more from them at their website. View Full User Profile

Betweenness Centrality

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In Network Analysis the identification of important nodes is a common task. We have various centrality measures that we can use and in this post we will focus on the Betweenness Centrality. We will see how this measure is computed and how to use the library networkx in order to create a visualization of the network where the nodes with the highest betweenness are highlighted. The betweenness focuses on the number of visits through the shortests paths. If a walker moves from one node to another node via the shortests path, then the nodes with a large number of visits have a higher centrality. The betweenness centrality is defined as 

where s(s,t) is total number of shortest paths from node s to node t and sv(s,t) is the number of those paths that pass through v
Let's see how to compute the betweenness with networkx. As first step we have to load a sample network (yes, it's the same of this post):

# read the graph (gml format)
G = nx.read_gml('lesmiserables.gml',relabel=True)
Now we have a representation G of our network and we can use the function betweenness_centrality() to compute the centrality of each node. This function returns a list of tuples, one for each node, and each tuple contains the label of the node and the centrality value. We can use this information in order to trim the original network and keep only the most important nodes:

def most_important(G):""" returns a copy of G with
     the most important nodes
     according to the pagerank """ 
 ranking = nx.betweenness_centrality(G).items()print ranking
 r =[x[1]for x in ranking]
 m = sum(r)/len(r)# mean centrality
 t = m*3# threshold, we keep only the nodes with 3 times the meanGt= G.copy()for k, v in ranking:if v < t:Gt.remove_node(k)returnGtGt= most_important(G)# trimming
And we can use the original network and the trimmed one to visualize the network as follows:

from pylab import show
# create the layout
pos = nx.spring_layout(G)# draw the nodes and the edges (all)
nx.draw_networkx_edges(G,pos,alpha=0.1)# draw the most important nodes with a different style
nx.draw_networkx_nodes(Gt,pos,node_color='r',alpha=0.4,node_size=254)# also the labels this time
The graph should be like this one: 

This graph is pretty interesting, indeed it highlights the nodes which are very influential on the way the information spreads over the network. In the sample network we used each node represents a character and the connection between two characters represent the coappearance in the same chapter of the book 'Les miserable'. Looking at the graph we can easily say what are the most important characters according to the Betweenness Centrality. We can also observe some interesting situations like the ones of Valjean and Myriel. They are to connected to groups of characters who don't have a direct connection with the main ones.

Published at DZone with permission of Giuseppe Vettigli, author and DZone MVB. (source)

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