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Hands-On Graph Analytics with Neo4j

Hands-On Graph Analytics with Neo4j

By : Estelle Scifo
4.6 (9)
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Hands-On Graph Analytics with Neo4j

Hands-On Graph Analytics with Neo4j

4.6 (9)
By: Estelle Scifo

Overview of this book

Neo4j is a graph database that includes plugins to run complex graph algorithms. The book starts with an introduction to the basics of graph analytics, the Cypher query language, and graph architecture components, and helps you to understand why enterprises have started to adopt graph analytics within their organizations. You’ll find out how to implement Neo4j algorithms and techniques and explore various graph analytics methods to reveal complex relationships in your data. You’ll be able to implement graph analytics catering to different domains such as fraud detection, graph-based search, recommendation systems, social networking, and data management. You’ll also learn how to store data in graph databases and extract valuable insights from it. As you become well-versed with the techniques, you’ll discover graph machine learning in order to address simple to complex challenges using Neo4j. You will also understand how to use graph data in a machine learning model in order to make predictions based on your data. Finally, you’ll get to grips with structuring a web application for production using Neo4j. By the end of this book, you’ll not only be able to harness the power of graphs to handle a broad range of problem areas, but you’ll also have learned how to use Neo4j efficiently to identify complex relationships in your data.
Table of Contents (18 chapters)
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1
Section 1: Graph Modeling with Neo4j
5
Section 2: Graph Algorithms
10
Section 3: Machine Learning on Graphs
14
Section 4: Neo4j for Production

Computing degree centrality

Computing degree centrality involves sorting nodes based on how many relationships they have. This can be computed with base Cypher or invoked via the GDS plugin and a projected graph.

Formula

Degree centrality Cn is defined as follows:

Cn = deg(n)

Here, deg(n) denotes the number of edges connected to the node n.

If your graph is directed, then you can define the incoming and outgoing degree as the number of relationships starting from node n and the number of relationships ending in n, respectively.

For instance, let's consider the following graph:

Node A has one incoming relationship (coming from B) and two outgoing relationships (to B and D), so its incoming degree is 1 and its outgoing degree is 2. The degrees of each node are summarized in the following table:

Node Outgoing degree Incoming degree Degree (undirected)
A 2 1 3
B 1 3 4
C 1 0 1
D 1 1 2

Let's now see how to get these results in Neo4j. You can create this small graph...

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