Social network analysis studies social relations using network theory. Nodes represent participants in a network. Lines between nodes represent relationships. Formally, this is called a graph. Due to the constraints of this book, we will only have a quick look at a simple graph that comes with the popular NetworkX Python library. matplotlib will help with the visualization of the graph.

Install NetworkX with the following commands:

$ pip install networkx
$ pip freeze|grep networkx
networkx==1.9

The import convention for NetworkX is as follows:

import networkx as nx

NetworkX provides a number of sample graphs, which can be listed as follows:

print [s for s in dir(nx) if s.endswith('graph')]

Load the Davis Southern women graph and plot a histogram of the degree of connections:

G = nx.davis_southern_women_graph()
plt.figure(1)
plt.hist(nx.degree(G).values())

The resulting histogram is shown as follows:

Draw the graph with node labels as follows:

plt.figure(2)
pos = nx.spring_layout...