(Ebook PDF) 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 1st edition by Estelle Scifo ISBN 9781804614907 1804614904 full chapters

(Ebook PDF) 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 1st edition by  Estelle Scifo-Ebook PDF Instant Download/Delivery:9781804614907, 1804614904

Instant download Full Chapter of 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 1st edition after payment

Product details:

ISBN 10: 1804614904

ISBN 13: 9781804614907

Author: Estelle Scifo

Table of contents:

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
• The complexity of graph data visualization
• Physical networks
• General case
• Visualizing a small graph with networkx and matplotlib
• Visualizing a graph with known coordinates
• Visualizing a graph with unknown coordinates
• Configuring object display
• Discovering the Neo4j Bloom graph application
• What is Bloom?
• Bloom installation
• Selecting data with Neo4j Bloom
• Configuring the scene in Bloom
• Visualizing large graphs with Gephi
• Installing Gephi and its required plugin
• Using APOC Extended to synchronize Neo4j and Gephi
• Configuring the view in Gephi
• Summary
• Further reading
• Exercises

Part 3 – Making Predictions on a Graph

• Chapter 6: Building a Machine Learning Model with Graph Features
• Technical requirements
• Introducing the GDS Python client
• GDS Python principles
• Input and output types
• Creating a projected graph from Python
• Running GDS algorithms from Python and extracting data in a dataframe
• write mode
• stream mode
• Dropping the projected graph
• Using features from graph algorithms in a scikit-learn pipeline
• Machine learning tasks with graphs
• Our task
• Computing features
• Extracting and visualizing data
• Building the model
• Summary
• Further reading
• Exercise
• Chapter 7: Automatically Extracting Features with Graph Embeddings for Machine Learning
• Technical requirements
• Introducing graph embedding algorithms
• Defining embeddings
• Graph embedding classification
• Using a transductive graph embedding algorithm
• Understanding the Node2Vec algorithm
• Using Node2Vec with GDS
• Training an inductive embedding algorithm
• Understanding GraphSAGE
• Introducing the GDS model catalog
• Training GraphSAGE with GDS
• Computing new node representations
• Summary
• Further reading
• Exercises
• Chapter 8: Building a GDS Pipeline for Node Classification Model Training
• Technical requirements
• The GDS pipelines
• What is a pipeline?
• Building and training a pipeline
• Creating the pipeline and choosing the features
• Setting the pipeline configuration
• Training the pipeline
• Making predictions
• Computing the confusion matrix
• Using embedding features
• Choosing the graph embedding algorithm to use
• Training using Node2Vec
• Training using GraphSAGE
• Summary
• Further reading
• Exercise
• Chapter 9: Predicting Future Edges
• Technical requirements
• Introducing the LP problem
• LP examples
• LP with the Netflix dataset
• Framing an LP problem
• LP features
• Topological features
• Features based on node properties
• Building an LP pipeline with the GDS
• Creating and configuring the pipeline
• Pipeline training and testing
• Summary
• Further reading
• Chapter 10: Writing Your Custom Graph Algorithms with the Pregel API in Java
• Technical requirements
• Introducing the Pregel API
• GDS’s features
• The Pregel API
• Implementing the PageRank algorithm
• The PageRank algorithm
• Simple Python implementation
• Pregel Java implementation
• Implementing the tolerance-stopping criteria
• Testing our code
• Test for the PageRank class
• Test for the PageRankTol class
• Using our algorithm from Cypher
• Adding annotations
• Building the JAR file
• Updating the Neo4j configuration
• Testing our procedure

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Tags:

Estelle Scifo,Graph Data Science,Neo4j