book

Graph Data Science with Neo4j

Name: Graph Data Science with Neo4j
Author: Estelle Scifo
ISBN: 9781804612743

by Estelle Scifo

January 2023

Intermediate to advanced

288 pages

6h 20m

English

Packt Publishing

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Graph Data Science with Neo4j
ContributorsAbout the authorAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesConventions usedGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Part 1 – Creating Graph Data in Neo4j
Chapter 1: Introducing and Installing Neo4j
Technical requirementsWhat is a graph database?DatabasesGraph databaseFinding or creating a graph databaseA note about the graph dataset’s formatModeling your data as a graphNeo4j in the graph databases landscapeNeo4j ecosystemSetting up Neo4jDownloading and starting Neo4j DesktopCreating our first Neo4j databaseCreating a database in the cloud – Neo4j AuraInserting data into Neo4j with Cypher, the Neo4j query languageExtracting data from Neo4j with Cypher pattern matchingSummaryFurther readingExercises
Chapter 2: Importing Data into Neo4j to Build a Knowledge Graph
Technical requirementsImporting CSV data into Neo4j with CypherDiscovering the Netflix datasetDefining the graph schemaImporting dataIntroducing the APOC library to deal with JSON dataBrowsing the datasetGetting to know and installing the APOC pluginLoading dataDealing with temporal dataDiscovering the Wikidata public knowledge graphData formatQuery language – SPARQLEnriching our graph with Wikidata informationLoading data into Neo4j for one personImporting data for all peopleDealing with spatial data in Neo4jImporting data in the cloudSummaryFurther readingExercises
Part 2 – Exploring and Characterizing Graph Data with Neo4j
Chapter 3: Characterizing a Graph Dataset
Technical requirementsCharacterizing a graph from its node and edge propertiesLink directionLink weightNode typeComputing the graph degree distributionDefinition of a node’s degreeComputing the node degree with CypherVisualizing the degree distribution with NeoDashInstalling and using the Neo4j Python driverCounting node labels and relationship types in PythonBuilding the degree distribution of a graphImproved degree distributionLearning about other characterizing metricsTriangle countClustering coefficientSummaryFurther readingExercises
Chapter 4: Using Graph Algorithms to Characterize a Graph Dataset
Technical requirementsDigging into the Neo4j GDS libraryGDS contentInstalling the GDS library with Neo4j DesktopGDS project workflowProjecting a graph for use by GDSNative projectionsCypher projectionsComputing a node’s degree with GDSstream modeThe YIELD keywordwrite modemutate modeAlgorithm configurationOther centrality metricsUnderstanding a graph’s structure by looking for communitiesNumber of componentsModularity and the Louvain algorithmSummaryFurther reading
Chapter 5: Visualizing Graph Data
Technical requirementsThe complexity of graph data visualizationPhysical networksGeneral caseVisualizing a small graph with networkx and matplotlibVisualizing a graph with known coordinatesVisualizing a graph with unknown coordinatesConfiguring object displayDiscovering the Neo4j Bloom graph applicationWhat is Bloom?Bloom installationSelecting data with Neo4j BloomConfiguring the scene in BloomVisualizing large graphs with GephiInstalling Gephi and its required pluginUsing APOC Extended to synchronize Neo4j and GephiConfiguring the view in GephiSummaryFurther readingExercises
Part 3 – Making Predictions on a Graph

Chapter 6: Building a Machine Learning Model with Graph Features
Technical requirementsIntroducing the GDS Python clientGDS Python principlesInput and output typesCreating a projected graph from PythonRunning GDS algorithms from Python and extracting data in a dataframewrite modestream modeDropping the projected graphUsing features from graph algorithms in a scikit-learn pipelineMachine learning tasks with graphsOur taskComputing featuresExtracting and visualizing dataBuilding the modelSummaryFurther readingExercise
Chapter 7: Automatically Extracting Features with Graph Embeddings for Machine Learning
Technical requirementsIntroducing graph embedding algorithmsDefining embeddingsGraph embedding classificationUsing a transductive graph embedding algorithmUnderstanding the Node2Vec algorithmUsing Node2Vec with GDSTraining an inductive embedding algorithmUnderstanding GraphSAGEIntroducing the GDS model catalogTraining GraphSAGE with GDSComputing new node representationsSummaryFurther readingExercises
Chapter 8: Building a GDS Pipeline for Node Classification Model Training
Technical requirementsThe GDS pipelinesWhat is a pipeline?Building and training a pipelineCreating the pipeline and choosing the featuresSetting the pipeline configurationTraining the pipelineMaking predictionsComputing the confusion matrixUsing embedding featuresChoosing the graph embedding algorithm to useTraining using Node2VecTraining using GraphSAGESummaryFurther readingExercise
Chapter 9: Predicting Future Edges
Technical requirementsIntroducing the LP problemLP examplesLP with the Netflix datasetFraming an LP problemLP featuresTopological featuresFeatures based on node propertiesBuilding an LP pipeline with the GDSCreating and configuring the pipelinePipeline training and testingSummaryFurther reading
Chapter 10: Writing Your Custom Graph Algorithms with the Pregel API in Java
Technical requirementsIntroducing the Pregel APIGDS’s featuresThe Pregel APIImplementing the PageRank algorithmThe PageRank algorithmSimple Python implementationPregel Java implementationImplementing the tolerance-stopping criteriaTesting our codeTest for the PageRank classTest for the PageRankTol classUsing our algorithm from CypherAdding annotationsBuilding the JAR fileUpdating the Neo4j configurationTesting our procedureSummaryFurther readingExercises
Index
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Content preview from Graph Data Science with Neo4j

6 Building a Machine Learning Model with Graph Features

So far, we have explored how to build and understand a graph dataset. We have computed some key metrics, such as the degree distribution. Using unsupervised algorithms, such as community detection, we have also better-identified graph structures. Finally, the graph visualization tools have enabled us to see the content of the dataset, and visually identify some aspects of the graph. Now, it is time to start applying this knowledge to build a machine learning (ML) model. In this chapter, we will introduce the Python client for the Graph Data Science (GDS) library, by allowing it to run graph algorithms directly from Python, without writing any Cypher. After computing and extracting our features ...

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Graph Data Science with Neo4j

by Estelle Scifo

6

Building a Machine Learning Model with Graph Features

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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