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Hands-On Graph Analytics with Neo4j: Perform graph processing and visualization techniques using connected data across your enterprise
Discover how to use Neo4j to identify relationships within complex and large graph datasets using graph modeling, graph algorithms, and machine learning
Key FeaturesGet up and running with graph analytics with the help of real-world examplesExplore various use cases such as fraud detection, graph-based search, and recommendation systemsGet to grips with the Graph Data Science library with the help of examples, and use Neo4j in the cloud for effective application scalingBook DescriptionNeo4j 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.
What you will learnBecome well-versed with Neo4j graph database building blocks, nodes, and relationshipsDiscover how to create, update, and delete nodes and relationships using Cypher queryingUse graphs to improve web search and recommendationsUnderstand graph algorithms such as pathfinding, spatial search, centrality, and community detectionFind out different steps to integrate graphs in a normal machine learning pipelineFormulate a link prediction problem in the context of machine learningImplement graph embedding algorithms such as DeepWalk, and use them in Neo4j graphsWho this book is forThis book is for data analysts, business analysts, graph analysts, and database developers looking to store and process graph data to reveal key data insights. This book will also appeal to data scientists who want to build intelligent graph applications catering to different domains. Some experience with Neo4j is required.
Table of ContentsGraph DatabasesThe Cypher Query LanguageEmpowering Your Business with Pure CypherThe Graph Data Science Library and Path FindingSpatial DataNode ImportanceCommunity Detection and Similarity MeasuresUsing Graph-based Features in Machine LearningPredicting RelationshipsGraph embedding - from Graphs to MatricesUsing Neo4j in Your Web ApplicationNeo4j at Scale
Key FeaturesGet up and running with graph analytics with the help of real-world examplesExplore various use cases such as fraud detection, graph-based search, and recommendation systemsGet to grips with the Graph Data Science library with the help of examples, and use Neo4j in the cloud for effective application scalingBook DescriptionNeo4j 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.
What you will learnBecome well-versed with Neo4j graph database building blocks, nodes, and relationshipsDiscover how to create, update, and delete nodes and relationships using Cypher queryingUse graphs to improve web search and recommendationsUnderstand graph algorithms such as pathfinding, spatial search, centrality, and community detectionFind out different steps to integrate graphs in a normal machine learning pipelineFormulate a link prediction problem in the context of machine learningImplement graph embedding algorithms such as DeepWalk, and use them in Neo4j graphsWho this book is forThis book is for data analysts, business analysts, graph analysts, and database developers looking to store and process graph data to reveal key data insights. This book will also appeal to data scientists who want to build intelligent graph applications catering to different domains. Some experience with Neo4j is required.
Table of ContentsGraph DatabasesThe Cypher Query LanguageEmpowering Your Business with Pure CypherThe Graph Data Science Library and Path FindingSpatial DataNode ImportanceCommunity Detection and Similarity MeasuresUsing Graph-based Features in Machine LearningPredicting RelationshipsGraph embedding - from Graphs to MatricesUsing Neo4j in Your Web ApplicationNeo4j at Scale
738 pages, Kindle Edition
Published August 21, 2020
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Displaying 1 - 2 of 2 reviews
November 2, 2022
Good introduction
This book offers a solid introduction to Neo4j. It sometimes gets off track, getting into the weeds of topics like user authentication in Flask, but overall it's worth the read if you want to learn about graph databases. Code examples use a range of languages, including Cypher, JavaScript, Python, and even SparQL.
This book offers a solid introduction to Neo4j. It sometimes gets off track, getting into the weeds of topics like user authentication in Flask, but overall it's worth the read if you want to learn about graph databases. Code examples use a range of languages, including Cypher, JavaScript, Python, and even SparQL.
October 22, 2021
If you only read one book on Neo4j this should be the one. Filled with mostly practical use-cases and peppered with just enough theory. This book will get you up and running by the 1st chapter. You can branch out from there into books with deeper theory if you want.
Displaying 1 - 2 of 2 reviews