Book description
Tackle the core challenges related to enterprise-ready graph representation and learning. With this hands-on guide, applied data scientists, machine learning engineers, and practitioners will learn how to build an E2E graph learning pipeline. Authors Ahmed Menshawy and Maraim Rizk Masoud from Mastercard's Cyber and Intelligence Division address core challenges at each pipeline stage, from data acquisition and representation to real-time inference and feedback loop retraining.
From their experience in building scalable and production-ready graph learning pipelines, the authors take you through the process of building the E2E graph learning pipeline in a world of dynamic and evolving graphs.
You'll learn how to:
- Understand the importance of graph learning for boosting enterprise-grade applications
- Navigate the challenges surrounding the development and deployment of enterprise-ready graph learning and inference pipelines
- Use traditional and advanced graph learning techniques to tackle graph use cases
- Use and contribute to PyGraf, an open source graph learning library, to help embed best practices while building graph applications
- Design and implement a graph learning algorithm using publicly available and syntactic data
- Apply privacy-preserved techniques to the graph learning process
Publisher resources
Table of contents
- Brief Table of Contents (Not Yet Final)
-
1. Introduction to Graphs
- The Power of Enterprise Graph Learning and Inference at Scale
- A Bird’s Eye View: Navigating the Book Chapters
- Graphs and Graph Learning
- Large-Scale Graphs in Real-World Enterprises: Use Cases
-
The Evolution of Graphs and Graph Learning: From Early Beginnings to Modern Applications
- Era 1: The Foundation of Graph Theory and Algorithms (1736-1970)
- Era 2: More Advanced in Graph Algorithms and Technologies (1970 - 1999)
- Era 3: Emergence of Graph Databases and Graph Query Languages (2000s-2006)
- Era 4: Graph Analytics and Traditional Machine Learning (2007-2011)
- Era 5: Rise of Graph Neural Networks (2012-2018)
- Era 6: Scalability, Robustness, and Enterprise Applications (2019-Present)
- Challenges of Enterprise-Ready Graph Learning Systems
- Summary
- 2. The Graph Pipeline
- 3. Traditional Machine Learning for Graphs
- About the Authors
Product information
- Title: Scaling Graph Learning for the Enterprise
- Author(s):
- Release date: August 2025
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781098146061
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