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 /

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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Scifo, Estelle (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing Ltd., 2023.
Edición:1st edition.
Temas:
Non-relational databases.
Graphic methods.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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
  • 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
  • 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
  • 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
  • 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

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