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)
1
Part 1: Getting Started with Graph Data Modeling
4
Part 2: Making the Graph Transition
7
Part 3: Storing and Productionizing Graphs
11
Part 4: Graphing Like a Pro

Storing a graph in Neo4j

With our graph database set up, and our methods for interacting with Neo4j written, we can start to use Python and Neo4j to store and explore our graph data.

In this section, we will be looking at an air travel network between the US and Canada and analyzing its properties to find efficient routes between locations.

Preprocessing data

To begin, let’s take a look at our data (sourced from Stanford University: https://snap.stanford.edu/data/reachability.html). We have two files, reachability_250.txt and reachability-meta.csv.

If we open reachability-meta.csv and take a look at the first few lines, we’ll find a list of information about cities in the US and Canada:

"node_id","name","metro_pop","latitude","longitude"
0,"Abbotsford, BC",133497.0,49.051575,-122.328849
1,"Aberdeen, SD",40878.0,45.45909,-98.487324
2,"Abilene, TX",166416.0,32.449175,-99.741424...