Book Image

Graph Data Science with Neo4j

By : Estelle Scifo
5 (1)
Book Image

Graph Data Science with Neo4j

5 (1)
By: Estelle Scifo

Overview of this book

Neo4j, along with its Graph Data Science (GDS) library, is a complete solution to store, query, and analyze graph data. As graph databases are getting more popular among developers, data scientists are likely to face such databases in their career, making it an indispensable skill to work with graph algorithms for extracting context information and improving the overall model prediction performance. Data scientists working with Python will be able to put their knowledge to work with this practical guide to Neo4j and the GDS library that offers step-by-step explanations of essential concepts and practical instructions for implementing data science techniques on graph data using the latest Neo4j version 5 and its associated libraries. You’ll start by querying Neo4j with Cypher and learn how to characterize graph datasets. As you get the hang of running graph algorithms on graph data stored into Neo4j, you’ll understand the new and advanced capabilities of the GDS library that enable you to make predictions and write data science pipelines. Using the newly released GDSL Python driver, you’ll be able to integrate graph algorithms into your ML pipeline. By the end of this book, you’ll be able to take advantage of the relationships in your dataset to improve your current model and make other types of elaborate predictions.
Table of Contents (16 chapters)
1
Part 1 – Creating Graph Data in Neo4j
4
Part 2 – Exploring and Characterizing Graph Data with Neo4j
8
Part 3 – Making Predictions on a Graph

The complexity of graph data visualization

In order for us to understand why graph visualization is so challenging, we are first going to investigate the easiest networks that can be visualized: physical networks.

Physical networks

By physical networks, I mean networks whose nodes (and sometimes edges) have fixed spatial positions (coordinates). That includes the following:

  • Road networks: Street intersections (nodes) have spatial coordinates (latitude and longitude). Edges (the roads themselves) also have a shape or geometry (linestring) that can be stored using a geospatial data format (shapefile or GeoJSON, for instance) and drawn on a map.
  • Public transport networks: Nodes are bus/train stops with defined positions; edges are the bus paths between these stops.
  • Electric network: We can imagine this as containing nodes with different types (power station, transformer, consumer, etc.). Each of them also has a precise location, and the distance between them can...