Summary
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 NetworkX
, igraph
(from igraph.org), and graph-tool
.
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. ...
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