We will create networks with the help of the networkx Python library.

The purpose of this project is to explore an important area of machine learning which
is to mine data that occurs naturally in the form of graphs. Graph data is present in a
number of applications, ranging from road networks, computer networks, social
networks, gene regulatory networks, diagnostic systems, to name just a few.
In this project we shall take the approach of synthetizing the data that we need. Rather
than sourcing real world data which is another approach that we could have taken, we
will rely on synthetic data. Using synthetic data has the advantage that we can
generate as much data as we desire and, most importantly, can configure the networks
according to our own objectives. These advantages enable us to model diverse
scenarios which in general is not possible with limited quantities of real-world data.
We will create networks with the help of the networkx Python library. The full
documentation for the library is available from: NetworkX Reference. Start-up code for
the project is available from the Modules folder in Canvas.
attached the document please refer to the whole thing