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Extract meaningful information from graph data with Neo4j's latest version 5

Use Graph Algorithms into a regular Machine Learning pipeline in Python

Learn the core principles of the Graph Data Science Library to make predictions and create data science pipelines.

Description

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.

Who is this book for?

If you’re a data scientist or data professional with a foundation in the basics of Neo4j and are now ready to understand how to build advanced analytics solutions, you’ll find this graph data science book useful. Familiarity with the major components of a data science project in Python and Neo4j is necessary to follow the concepts covered in this book.

What you will learn

Use the Cypher query language to query graph databases such as Neo4j

Build graph datasets from your own data and public knowledge graphs

Make graph-specific predictions such as link prediction

Explore the latest version of Neo4j to build a graph data science pipeline

Run a scikit-learn prediction algorithm with graph data

Train a predictive embedding algorithm in GDS and manage the model store

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Causal Inference and Discovery in Python

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kbpr Mar 14, 2023

Graph Data Science with Neo4j is a fantastic overview for those starting out in their graph data science journey. The author walks the reader through everything from installation, graph theory and graph algorithms, all the way to supervised machine learning pipelines. She has very clear descriptions and explanations throughout, building up concepts step by step to *show* the reader how to interact with graphs, rather than “telling” and providing a list of features. The result is a solid jumping off point for aspiring graph data scientists, with many great tidbits for the more experienced graph practitioners to return to and reference.

Amazon Verified review

Phani Dathar Mar 17, 2023

Estelle has done an excellent job of making this book a must read for both the beginners and graph practitioners alike. Given her extensive experience building solutions using graph technology, the practical advice to the readers on how to leverage Neo4j graph database and Neo4j graph data science to address real-world use cases is very valuable.The book starts with step-by-step walkthrough of creating a graph database, introduction to cypher and easy-to-follow examples of ingesting data and building a knowledge graph. It is very important for readers to understand how to store and extract the connections in their data and understand the topology of the network before using graph machine learning and/or building applications on graph databases. The first three chapters in this book combined with the chapter on graph visualization do an excellent job in laying that foundation for moving on to the advanced concepts in the graph data science world.Graph algorithms and graph machine learning are the advanced topics that are also covered in this book with a good set of explanations on why, where and how-to use them. Leveraging graph algorithms to generate graph features, graph embeddings for dimensionality reduction, building machine learning pipelines by integrating graph features are all explained very well and author's expertise shines through in this book.I would recommend this book as a must-read for developers, data scientists and analysts who are starting their journey with graph technology and graph data science as well as graph practitioners who are familiar with the concepts of network science.

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Nathan Smith Mar 30, 2023

The examples in this book are really useful, and people to whom I have recommended the book have also liked it. The author does a good job of walking you through the whole graph data science lifecycle, end-to-end. It is especially valuable in providing guidance that goes beyond the Neo4j product documentation on complex topics like link prediction pipelines. I have not seen a better explanation of the Neo4j Pregel API anywhere.

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Om S Mar 13, 2023

Graph Data Science with Neo4j" is a practical guide for data scientists who are looking to enhance their skill set by learning how to extract meaningful information from graph data. The book covers various essential concepts and practical instructions for implementing data science techniques on graph data using the latest Neo4j version 5 and its associated libraries. The book is divided into ten chapters, starting with an introduction to Neo4j, installation, and building a knowledge graph.The book then moves on to characterizing a graph dataset and using graph algorithms to analyze it. Readers will also learn how to visualize graph data and build a machine learning model with graph features. The book introduces readers to the newly released GDSL Python driver, which allows for the integration of graph algorithms into a machine learning pipeline.The latter part of the book covers more advanced topics such as building a GDS pipeline for node classification model training and predicting future edges. The book concludes by teaching readers how to write their custom graph algorithm with the Pregel API.Overall, "Graph Data Science with Neo4j" is a useful and practical guide for data scientists looking to learn about graph data science. The book covers a range of topics, from the basics to advanced techniques. The step-by-step instructions and real-world examples make the book easy to follow and understand.

Amazon Verified review

Shanthababu Pandian May 26, 2023

Data Science is a buzzing word in recent eras; collecting data from various sources and representing them is a challenging task. A graph is a mathematical object set and derived by vertices or nodes and edges. It has the characteristics of being easily traversed, going from one node to another by following edges.The author started his innings by defining what is a graph database in Part 1 and answering by stating that “Data is saved into nodes, which can be connected through edges to model relationships between them”The author introduces the Neo4j ecosystem and outlines the Neo4j Browser, Neo4j Bloom, and Neodash. Helping with clear steps to create a Neo4j database, inserting data into Neo4j with Cypher, the Neo4j query language. Importing Data into Neo4j to Build a Knowledge Graph.Importing Data into Neo4j to Build a Knowledge Graph using CSV data into Neo4j with Cypher with the classical data from netflix.zip from defining the graph schema to creating nodes and relationships the author provided the detailed steps. Introducing Awesome Procedures on Cypher (APOC) Neo4j plugin and playing with JSON data is a great credit for readers to understand how to utilise the Neo4j environment.In Part 2 the author takes us on a tour of exploring and characterising Graph Data with Neo4j, where we can learn how to characterise a graph dataset, Neo4j Graph Data Science (GDS) library and the most common graph algorithms to analyse the graph topology. Characterising a graph from its node and edge properties like Link direction, Link weight and Node type.Coming to computing the graph degree distribution author gives an idea of the definition of a node’s degree Incoming, Outgoing and Total degree precisely. Help us to understand computing the node degree with Cypher, Building the degree distribution of a graph and how to Improve degree distribution.Under characterising metrics, the author gives heads-up on Triangle count, various clustering coefficients, and their calculations. Furthermore, digging into the Neo4j GDS library and installing the same with Neo4j Desktop is a power pack for the reader and makes them hands-on concerning GDS.Visualizing Graph Data is the final goal for Graph data, the author has provided a detailed walkthrough of visualising the complexity of graph data visualisation, small graph with networkx and matplotlib and large graphs with Gephi.In the end, we have to make the prediction, the author has covered this in Part 3 as Making Predictions on a Graph. In this part. Here the author re-introduces a well-known Python library, namely sci-kit-learn, and extracts data from Neo4j to build a model and how the GDS library helps us to embeddings to build node classification and link prediction pipelines.Build a Machine Learning Model with Graph Features using the GDS Python client and explain how the GDS library is allowing it to run graph algorithms directly from Python without writing any Cypher.The author has provided a very detailed note on graph embedding techniques and algorithms – classification, Node2Vec and building a GDS pipeline for classification model and trainingand predicting future edges are fruitful topics and must-read.Overall … I can give 4.0/5.0 for this. Certainly, a special effort from the author is really much appreciable.-Shanthababu PandianArtificial Intelligence and Analytics | Cloud Data and ML Architect | Scrum MasterNational and International Speaker | Blogger |

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Graph Data Science with Neo4j: Learn how to use Neo4j 5 with Graph Data Science library 2.0 and its Python driver for your project

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