Book Image

Graph Data Modeling in Python

By : Gary Hutson, Matt Jackson
Book Image

Graph Data Modeling in Python

By: Gary Hutson, Matt Jackson

Overview of this book

Graphs have become increasingly integral to powering the products and services we use in our daily lives, driving social media, online shopping recommendations, and even fraud detection. With this book, you’ll see how a good graph data model can help enhance efficiency and unlock hidden insights through complex network analysis. Graph Data Modeling in Python will guide you through designing, implementing, and harnessing a variety of graph data models using the popular open source Python libraries NetworkX and igraph. Following practical use cases and examples, you’ll find out how to design optimal graph models capable of supporting a wide range of queries and features. Moreover, you’ll seamlessly transition from traditional relational databases and tabular data to the dynamic world of graph data structures that allow powerful, path-based analyses. As well as learning how to manage a persistent graph database using Neo4j, you’ll also get to grips with adapting your network model to evolving data requirements. By the end of this book, you’ll be able to transform tabular data into powerful graph data models. In essence, you’ll build your knowledge from beginner to advanced-level practitioner in no time.
Table of Contents (16 chapters)
Part 1: Getting Started with Graph Data Modeling
Part 2: Making the Graph Transition
Part 3: Storing and Productionizing Graphs
Part 4: Graphing Like a Pro

Knowledge graph analysis and community detection

Before we begin to delve deeper into our knowledge graph, it makes sense to get an understanding of its structure. Information on our graph’s structure will frame further analysis that we carry out. This analysis will be pivotal and we will examine how to examine the knowledge graph structure by looking at which components are connected; conversely, disconnected components would mean that there are abstracts in our knowledge graph that are standalone terms and abstracts that are separate from our main knowledge of medical abstracts. From there, we will look at common terms and methods to identify abstracts of interest. Let’s get started by examining our knowledge graph structure.

Examining the knowledge graph structure

The next series of steps in our process will be to examine the knowledge graph structure:

  1. Let’s first find the number of nodes and edges in our graph, since in this chapter, we have constructed...