| Description |
1 online resource (288 pages) : illustrations |
| Bibliography |
Includes bibliographical references and index. |
| Summary |
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. |
| Contents |
Cover -- Copyright -- Contributors -- Table of Contents -- Preface -- Part 1 -- Creating Graph Data in Neo4j -- Chapter 1: Introducing and Installing Neo4j -- Technical requirements -- What is a graph database? -- Databases -- Graph database -- Finding or creating a graph database -- A note about the graph dataset's format -- Modeling your data as a graph -- Neo4j in the graph databases landscape -- Neo4j ecosystem -- Setting up Neo4j -- Downloading and starting Neo4j Desktop -- Creating our first Neo4j database -- Creating a database in the cloud -- Neo4j Aura |
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Inserting data into Neo4j with Cypher, the Neo4j query language -- Extracting data from Neo4j with Cypher pattern matching -- Summary -- Further reading -- Exercises -- Chapter 2: Importing Data into Neo4j to Build a Knowledge Graph -- Technical requirements -- Importing CSV data into Neo4j with Cypher -- Discovering the Netflix dataset -- Defining the graph schema -- Importing data -- Introducing the APOC library to deal with JSON data -- Browsing the dataset -- Getting to know and installing the APOC plugin -- Loading data -- Dealing with temporal data |
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Discovering the Wikidata public knowledge graph -- Data format -- Query language -- SPARQL -- Enriching our graph with Wikidata information -- Loading data into Neo4j for one person -- Importing data for all people -- Dealing with spatial data in Neo4j -- Importing data in the cloud -- Summary -- Further reading -- Exercises -- Part 2 -- Exploring and Characterizing Graph Data with Neo4j -- Chapter 3: Characterizing a Graph Dataset -- Technical requirements -- Characterizing a graph from its node and edge properties -- Link direction -- Link weight -- Node type |
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Computing the graph degree distribution -- Definition of a node's degree -- Computing the node degree with Cypher -- Visualizing the degree distribution with NeoDash -- Installing and using the Neo4j Python driver -- Counting node labels and relationship types in Python -- Building the degree distribution of a graph -- Improved degree distribution -- Learning about other characterizing metrics -- Triangle count -- Clustering coefficient -- Summary -- Further reading -- Exercises -- Chapter 4: Using Graph Algorithms to Characterize a Graph Dataset -- Technical requirements |
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Digging into the Neo4j GDS library -- GDS content -- Installing the GDS library with Neo4j Desktop -- GDS project workflow -- Projecting a graph for use by GDS -- Native projections -- Cypher projections -- Computing a node's degree with GDS -- stream mode -- The YIELD keyword -- write mode -- mutate mode -- Algorithm configuration -- Other centrality metrics -- Understanding a graph's structure by looking for communities -- Number of components -- Modularity and the Louvain algorithm -- Summary -- Further reading -- Chapter 5: Visualizing Graph Data -- Technical requirements |
| Subject |
Non-relational databases.
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Graphic methods.
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Bases de données non relationnelles. |
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Méthodes graphiques. |
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graphs. |
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Graphic methods |
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Non-relational databases |
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