book

Graph Data Modeling in Python

by Gary Hutson, Matt Jackson

June 2023

Intermediate to advanced

236 pages

6h 25m

English

Packt Publishing

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ContributorsAbout the authorsAbout the reviewer
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
Technical requirementsWhy should you use graphs?Composite components of a graphThe fundamentals of nodes and edges and the properties of a graphUndirected graphsDirected graphsNode propertiesHeterogeneous graphsSchema designComparing RDBs and GDBsGDBs to the rescueThe use of graphs across various industriesIntroduction to NetworkX and igraphNetworkX basicsigraph basicsSummary
Technical requirementsMaking the transition from tabular to graph dataExamining the dataDesigning a schemaImplementing the model in PythonAdding nodes and attributesAdding edgesWriting a generic graph import methodThe most popular TV show – a real-world use caseExamining the graph structureMeasuring connectednessLooking at the top degree nodesUsing select() to interrogate the graphProperties of our popular nodesSummary
Technical requirementsRecommending a game to a userInstalling MySQLSetting up a MySQL databaseQuerying MySQL in PythonExamining the data in PythonPath-based analytics in tabular dataFrom relational to graph databasesSchema designIngestion considerationsPath-based analytics in igraphOur recommendation systemGeneric MySQL to igraph methodsA more advanced recommendation system using Jaccard similaritySummary
Technical requirementsIntroducing knowledge graphsCleaning the data for our knowledge graphIngesting data into a knowledge graphDesigning a knowledge graph schemaLinking text to termsConstructing the knowledge graphKnowledge graph analysis and community detectionExamining the knowledge graph structureIdentifying abstracts of interestIdentifying fields with community detectionSummary
Technical requirementsUsing graph databasesNeo4j as a graph databaseThe Cypher query languageQuerying Neo4j from PythonStoring a graph in Neo4jPreprocessing dataMoving nodes, edges, and properties to Neo4jOptimizing travel with Python and CypherTravel recommendationsMoving to ingestion pipelinesSummary

Technical requirementsGraph pipeline developmentA graph database for retailDesigning a schema and pipelineSetting up a new databaseSchema designAdding static product informationSimulating customer interactionsMaking product recommendationsProduct recommendations by brandDrawing on other customers purchasesUsing similarity scores to recommend productsSummary
Technical requirementsRefactoring reasoningChange in relational and graph databasesEffectively evolving with graph schema designPutting the changes into developmentInitializing a new databaseAdding constraintsPre-change schemaUpdating the schemaSummary
Technical requirementsWhat are projections?How to use a projectionCreating a projection in igraphCreating a projection in Neo4jPutting the projection to workAnalyzing the igraph actor projectionExploring connected componentsExploring cliques in our graphAnalyzing the Neo4j film projectionSummary
Technical requirementsDebugging graph issuesCommon igraph issuesNo nodes in the graphNode IDs in igraphAdding propertiesUsing the select methodChained statements and selectEfficiency and path lengthsCommon Neo4j issuesSlow writing from file to Neo4jIndexing for query performanceCaching resultsMemory limitationsHandling duplicates with MERGEHandling duplicates with constraintsEXPLAIN, PROFILE, and the eager operatorSummary
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Overview

Dive into the world of graph databases with "Graph Data Modeling in Python". This book offers a practical approach to mastering the skills needed for curating, analyzing, and visualizing data with graphs. Whether you're a data scientist or developer, you'll find step-by-step guidance on converting traditional data into effective graph data structures.

What this Book will help me do

Understand and apply graph data modeling principles using Python.
Learn to effectively store, query, and transform graph structures using libraries like NetworkX and igraph.
Master schema design for graph databases with real-world examples and use cases.
Implement and practice graph analytics for insights like community detection and recommendation engines.
Transition from relational or tabular data models to graph-based solutions and workflows.

Author(s)

Gary Hutson and Matt Jackson are skilled data scientists and software developers with substantial experience in Python programming and graph data analysis. They combine their technical expertise with a passion for teaching to make graph data modeling accessible and practical. Their pragmatic approach allows readers to quickly apply learned techniques to real-world projects.

Who is it for?

This book is ideal for data analysts and database developers eager to harness the power of graph databases and for Python developers starting their journey in data science. If you're looking to understand and novelly use data frameworks, and are comfortable with Python basics, this book will guide you to the next level in your graph modeling knowledge.