Summary

In this chapter, we looked at why you should start to think graph, from the benefits of why these methods are becoming the most widely utilized and discussed approaches in various industries. We looked at what a graph is and explained the various types of graphs, such as graphs that are concerned with how things move through a network, to influence graphs (who is influencing who on social media), to graph methods to identify groups and interactions, and how graphs can be utilized to detect patterns in a network.

Moving on from that, we examined the fundamentals of what makes up a graph. Here, we looked at the fundamental elements of nodes, edges, and properties and delved into the difference between an undirected and directed graph. Additionally, we examined the properties of nodes, looked at heterogeneous graphs, and examined the types of schema contained within a graph.

This led to how GDBs compare to legacy RDBs and why, in many cases, graphs are much easier and faster to transverse and query, with examples of how graphs can be utilized to optimize the stops on a train journey and how this can be extended, with ease, to add bus stops as well, as a new data source.

Following this, we looked at how graphs are being deployed across various industries and some use cases for why graphs are important in those industries, such as fraud detection in the finance sector, intelligence profiling in the government sector, patient journeys in hospitals, churn across networks, and customer segmentation in marketing. Graphs truly are becoming ubiquitous across various industries.

We wrapped up this chapter by providing an introduction to the powerhouses of graph analytics and network analysis – igraph and NetworkX. We showed you how, in a few lines of Python code, you can easily start to populate a graph.

In the next chapter, we will look at how to work with and create graph data models. The next chapter will contain many more hands-on examples of how to structure your data using graph data models in Python.