Summary

In this chapter, we looked at many of the concepts you need to learn when working with graph data models. We started off by looking at making the transition from tabular data files to building nodes, attributes, edges, and edge lists. From there, we then delved into considerations for designing a schema, focusing on a common type of graph in social networks—an undirected heterogeneous graph.

This stood us in good stead for then implementing the model in Python, which focused on the following key methods of building graphs with igraph. First, we looked at adding nodes and attributes to your graph—here, we started with the creation of nodes, then we added attributes for these nodes. Nodes in a graph can be thought of as properties in other object-oriented languages. Next, we looked at the creation of edges to connect your nodes or relationships to the nodes, and we discussed what is meant by an edgelist—a list of relationships (edges) describing connectivity...