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

Introducing knowledge graphs

In complex fields, such as science and medicine, the sheer amount of data and literature available on specific topics is hard to overstate. The same goes for knowledge management in established companies and industries where, over time, institutional knowledge in the form of textual information builds up, becoming too large to sensibly disseminate. In both of these cases, a knowledge graph may help to alleviate issues associated with too much disparate information.

The aim of a knowledge graph is to link together related information, text, and documents in a sensible and searchable way.

In the case of knowledge graphs using text, links in a graph often represent related documents or articles. Text processing and NLP are huge fields in themselves, so for the purposes of this chapter, we will be keeping methods for working with text simple. Of course, the quality of text data has a large impact on the preparation of data for knowledge graph ingestion...