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.
publication date
2023-06-30
what you will learn
Design graph data models and master schema design best practices
Work with the NetworkX and igraph frameworks in Python Store, query, ingest, and refactor graph data
Store your graphs in memory with Neo4j
Build and work with projections and put them into practice
Refactor schemas and learn tactics for managing an evolved graph data model
key features
Transform relational data models into graph data model while learning key applications along the way * Discover common challenges in graph modeling and analysis, and learn how to overcome them * Practice real-world use cases of community detection, knowledge graph, and recommendation network
approach
A step-by-step explanation and walkthroughs of foundational level graph concepts to building hands-on data structures ready for graph modeling, and finally putting it to practice on real-world case studies like the creation of social media graphs knowledge graphs
audience
If you are a data analyst or database developer interested in learning graph databases and how to curate and extract data from them, this is the book for you. It is also beneficial for data scientists and Python developers looking to get started with graph data modeling. Although knowledge of Python is assumed, no prior experience in graph data modeling theory and techniques is required.
short description
Graph Data Modeling with Python is your guide to building real-world use cases of how to prepare graph data for modeling, converting tabular to graph data structures, and working through simple graph use cases using degree centrality. Using the Python packages igraph and NetworkX, you’ll build your skills in various types of graph analytics.
subtitle
A practical guide to curating, analyzing, and modeling data with graphs
keywords
Python data science, data modeling books, data modelling, knowledge graph
Product ISBN
9781804618035
