Getting Started with Graphs

Graphs are mathematical structures that are used for describing relationships between entities, and they are used almost everywhere. They can be used for representing maps, where cities are linked through streets. Graphs can describe biological structures, web pages, and even the progression of neurodegenerative diseases. For example, social networks are graphs, where users are connected by links representing the “follow” relationship.

Graph theory, the study of graphs, has received major interest for years, leading people to develop algorithms, identify properties, and define mathematical models to better understand complex behaviors.

This chapter will review some of the concepts behind graph-structured data. Theoretical notions will be presented, together with examples to help you understand some of the more general concepts and put them into practice. In this chapter, we will introduce and use some of the most widely used Python libraries for the creation, manipulation, and study of the structure dynamics and functions of complex networks.

The following topics will be covered in this chapter:

• General information on the practical exercises and how to set up the Python environment to run them
• Introduction to graphs with networkx
• Plotting graphs
• Graph properties
• Benchmarks and repositories
• Dealing with large graphs