Hutson / Jackson Graph Data Modeling in Python

A practical guide to curating, analyzing, and modeling data with graphs
1. Auflage 2023
ISBN: 978-1-80461-934-6
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 236 Seiten

Graphs have become increasingly integral to powering the products and services we use in our daily lives, driving social media, online shopping recommendations, and even fraud detection. With this book, you'll see how a good graph data model can help enhance efficiency and unlock hidden insights through complex network analysis.
Graph Data Modeling in Python will guide you through designing, implementing, and harnessing a variety of graph data models using the popular open source Python libraries NetworkX and igraph. Following practical use cases and examples, you'll find out how to design optimal graph models capable of supporting a wide range of queries and features. Moreover, you'll seamlessly transition from traditional relational databases and tabular data to the dynamic world of graph data structures that allow powerful, path-based analyses. As well as learning how to manage a persistent graph database using Neo4j, you'll also get to grips with adapting your network model to evolving data requirements.
By the end of this book, you'll be able to transform tabular data into powerful graph data models. In essence, you'll build your knowledge from beginner to advanced-level practitioner in no time.

Autoren/Hrsg.

Hutson, Gary

Jackson, Matt

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse

Inhaltsverzeichnis

Table of Contents - Introducing Graphs in the Real World
- Working with Graph Data Models
- Data Model Transformation – Relational to Graph Databases
- Building a Knowledge Graph
- Working with Graph Databases
- Pipeline Development

- Refactoring and Evolving Schemas
- Perfect Projections
- Common Errors and Debugging

Über Autor(innen)

Hutson Gary:

Gary Hutson is an experienced Python and graph database developer. He has experience in Python, R, C, SQL, and many other programming languages, and has been working with databases of some form for 20+ years. Professionally, he works as the Head of Graph Data Science and Machine Learning for a company that uses machine learning (ML) and graph data science techniques to detect risks on social media and other platforms. He is experienced in many graph and ML techniques, specializing in natural language processing, computer vision, deep learning, and ML. His passion is using open sourced technologies to create useful toolsets and practical applied solutions, as this was the focus of his master's degree.Jackson Matt:

Matt Jackson is a lead data scientist specializing in graph theory and network analytics. His interest in graphs was sparked during his PhD in systems biology, where network analysis was used to uncover novel features of cell organization. Since then, he has worked in diverse industries - from academia to intelligence, highlighting patterns and risk in complex data by harnessing the latest in graph algorithms and machine learning.