| Description |
1 online resource |
| Summary |
Discover how to use Neo4j to identify relationships within complex and large graph datasets using graph modeling, graph algorithms, and machine learning Key Features Get up and running with graph analytics with the help of real-world examples Explore various use cases such as fraud detection, graph-based search, and recommendation systems Get to grips with the Graph Data Science library with the help of examples, and use Neo4j in the cloud for effective application scaling Book Description 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. What you will learn Become well-versed with Neo4j graph database building blocks, nodes, and relationships Discover how to create, update, and delete nodes and relationships using Cypher querying Use graphs to improve web search and recommendations Understand graph algorithms such as pathfinding, spatial search, centrality, and community detection Find out different steps to integrate graphs in a normal machine learning pipeline Formulate a link prediction problem in the context of machine learning Implement graph embedding algorithms such as DeepWalk, and use them in Neo4j graphs Who this book is for This book is for data analysts, bus ... |
| Contents |
Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: Graph Modeling with Neo4j -- Chapter 1: Graph Databases -- Graph definition and examples -- Graph theory -- A bit of history: the Seven Bridges of Königsberg problem -- Graph definition -- Visualization -- Examples of graphs -- Networks -- Road networks -- Computer networks -- Social networks -- Your data is also a graph -- Moving from SQL to graph databases -- Database models -- SQL and joins -- It's all about relationships |
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Neo4j -- the nodes, relationships, and properties model -- Building blocks -- Nodes -- Relationships -- Properties -- SQL to Neo4j translator -- Neo4j use cases -- Understanding graph properties -- Directed versus undirected -- Weighted versus unweighted -- Cyclic versus acyclic -- Dense versus sparse -- Graph traversal -- Connected versus disconnected -- Considerations for graph modeling in Neo4j -- Relationship orientation -- Node or property? -- Summary -- Further reading -- Chapter 2: The Cypher Query Language -- Technical requirements -- Creating nodes and relationships |
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Managing databases with Neo4j Desktop -- Creating a node -- Selecting nodes -- Filtering -- Returning properties -- Creating a relationship -- Selecting relationships -- The MERGE keyword -- Updating and deleting nodes and relationships -- Updating objects -- Updating an existing property or creating a new one -- Updating all properties of the node -- Updating node labels -- Deleting a node property -- Deleting objects -- Pattern matching and data retrieval -- Pattern matching -- Test data -- Graph traversal -- Orientation -- The number of hops -- Variable-length patterns -- Optional matches |
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Using aggregation functions -- Count, sum, and average -- Creating a list of objects -- Unnesting objects -- Importing data from CSV or JSON -- Data import from Cypher -- File location -- Local file: the import folder -- Changing the default configuration to import a file from another directory -- CSV files -- CSV files without headers -- CSV files with headers -- Eager operations -- Data import from the command line -- APOC utilities for imports -- CSV files -- JSON files -- Importing data from a web API -- Setting parameters -- Calling the GitHub web API -- Summary of import methods |
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Measuring performance and tuning your query for speed -- Cypher query planner -- Neo4j indexing -- Back to LOAD CSV -- The friend-of-friend example -- Summary -- Questions -- Further reading -- Chapter 3: Empowering Your Business with Pure Cypher -- Technical requirements -- Knowledge graphs -- Attempting a definition of knowledge graphs -- Building a knowledge graph from structured data -- Building a knowledge graph from unstructured data using NLP -- NLP -- Neo4j tools for NLP -- GraphAware NLP library -- Importing test data from the GitHub API -- Enriching the graph with NLP |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Graph databases.
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Graph databases |
| Other Form: |
Print version: Scifo, Estelle. Hands-On Graph Analytics with Neo4j : Perform Graph Processing and Visualization Techniques Using Connected Data Across Your Enterprise. Birmingham : Packt Publishing, Limited, ©2020 |
| ISBN |
1839215666 (electronic book) |
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9781839215667 (electronic bk.) |
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9781839212611 (paperback) |
| Standard No. |
9781839212611 |
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