This chapter illustrates the examples of networks and bioinformatics and the choice of Python packages to be able to plot the results. We looked at a brief introduction to graphs and multigraphs and used the sparse matrix and distance graphs to illustrate how you can store and display graphs with several different packages, such as
igraph (from igraph.org), and
The clustering coefficient and centrality of graphs demonstrates how you can compute clustering coefficients so that they are able to know how significant a node or vertex is in the graph. We also looked at the analysis of social network data with an illustration of Twitter friends and followers visually, using the
Python-Twitter package and the
NetworkX library. ...