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Hands-On Graph Analytics with Neo4j

Hands-On Graph Analytics with Neo4j

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

Hands-On Graph Analytics with Neo4j

4.6 (9)
By: 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

Graph neural networks

GNNs were introduced in 2005 and have received a lot of attention during the last 5 years or so. The key concept behind them is to try to generalize the ideas behind CNNs and RNNs to apply them to any type of dataset, including graphs. This section is only a short introduction to GNNs, since we would require an entire book to fully explore the topic. As usual, more references are given in the Further reading section if you would like to gain a deeper understanding of this topic.

Extending the principles of CNNs and RNNs to build GNNs

CNNs and RNNs both involve aggregating information from a neighborhood in a special context. For RNNs, the context is a sequence of inputs (words, for instance) and a sequence is nothing more than a special type of graph. The same applies to CNNs, which are used to analyze images, or pixel grids, which are also a special type of graph where each pixel is connected to its adjacent pixels. It is logical therefore to try and use neural...

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