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

Ingesting data into a knowledge graph

There is a lot to consider before jumping straight into creating a knowledge graph from our cleaned abstract data. As with previous chapters, we must consider the structure of the graph we are aiming to produce first. We will then need to process our abstracts to extract terms of interest. Then, once we have terms, we can create a list of edges to import into igraph.

Getting the ingestion right into the knowledge graph is crucial and this all stems from how you conceptually and practically design your graph schema. The following section shows how to design your schema to make sure your knowledge graph works the way you expect it to.

Designing a knowledge graph schema

Before jumping straight into data ingestion, we must consider the structure of our knowledge graph. For our use case, we’re interested in connecting related documents and concepts.

In terms of nodes, we have both abstracts and terms. Our abstracts have only an ID...