Scaling Graph Learning for the Enterprise

Scaling Graph Learning for the Enterprise

by Ahmed Menshawy, Sameh Mohamed, Maraim Rizk Masoud
Released August 2025
Publisher(s): O'Reilly Media, Inc.
ISBN: 9781098146061

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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. You'll explore core challenges at each pipeline stage, from data acquisition and representation to real-time inference and feedback loop retraining.

Drawing on their experience 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

Ahmed Menshawy is the vice president of AI engineering in Mastercard's Cyber and Intelligence Division; Sameh Mohamed is an expert in machine learning and health informatics; and Maraim Rizk Masoud is a lead ML engineer.

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Table of contents

  1. Brief Table of Contents (Not Yet Final)
  2. 1. Introduction to Graphs
    1. The Power of Enterprise Graph Learning and Inference at Scale
    2. A Bird’s Eye View: Navigating the Book Chapters
    3. Graphs and Graph Learning
      1. What is a Graph?
      2. Graph Data Representation
      3. Graph Learning
      4. Scalable Graph Learning: Addressing the Requirements
      5. Advantages of Scalable Graph Learning in Enterprise
    4. Large-Scale Graphs in Real-World Enterprises: Use Cases
      1. Travel-time predictions on Google Maps
      2. Drug development: Halicin
      3. Fraud detection
    5. The Evolution of Graphs and Graph Learning: From Early Beginnings to Modern Applications
      1. Era 1: The Foundation of Graph Theory and Algorithms (1736-1970)
      2. Era 2: More Advanced in Graph Algorithms and Technologies (1970 - 1999)
      3. Era 3: Emergence of Graph Databases and Graph Query Languages (2000s-2006)
      4. Era 4: Graph Analytics and Traditional Machine Learning (2007-2011)
      5. Era 5: Rise of Graph Neural Networks (2012-2018)
      6. Era 6: Scalability, Robustness, and Enterprise Applications (2019-Present)
    6. Challenges of Enterprise-Ready Graph Learning Systems
      1. Data Harmonization Challenges
      2. Computationally Intensive Workloads
      3. Dynamic Evolving Graphs
      4. Active Monitoring and Drift Detection
      5. Real Time-Inference
    7. Summary
  3. 2. The Graph Pipeline
    1. The Enterprise Graph Learning Training and Inference Pipeline
      1. Graph Data Pipeline Overview
      2. GML Training Pipeline Overview
      3. GML Inference Pipeline Overview
    2. Graph Data Pipeline
      1. Definition of Graph Data
      2. Graph Data Sourcing and Understanding
      3. Graph Data Preparation
    3. Summary
  4. 3. Traditional Machine Learning for Graphs
    1. Approaches to Graph ML
      1. Traditional graph-based machine learning
      2. Non-traditional graph-based machine learning
    2. Representing Graph for Traditional ML
      1. Graph Representation
      2. Representing Amazon Co-purchasing Networks as Graphs
      3. Navigating Graph Tasks in the Amazon Co-purchasing Dataset
    3. Graph Feature Engineering
      1. Importance and Challenges
      2. Types of Graph Features
      3. Hands-On: Extracting Features for Amazon Co-purchasing Graph
    4. Graph Features in ML Modeling
      1. Task and Techniques Overview
      2. Predicting High-Rated Products
    5. Feature Learning with Node Embeddings
      1. Random Walk Algorithm
      2. Amazon Co-purchasing Dataset and Node Embeddings
    6. Summary
  5. 4. PyGraf: End-to-End Graph Learning and Serving
    1. Graph Libraries Overview
      1. Challenges of Open Source Graph Libraries: PyGraf Opportunities
      2. PyGraf: A Solution for Streamlined Graph Learning and Serving
    2. Introduction to PyGraf
      1. PyGraf Key Features
      2. PyGraf Purposes: Empowering Dynamic Environments
    3. Architecture and Core Capabilities
      1. Core Components Layer: An Overview
      2. Adaptation and Integration Layer: An Overview
      3. Best Practices Layer: An Overview
    4. In-Depth Exploration of Core Library Components
      1. Data Component
      2. Training Component
      3. Serving Component
      4. Privacy Preserving Component
    5. End-to-End Example Using PyGraf: Amazon Co-Purchase Dataset
      1. Preprocessing and Transformation
      2. Model Training
      3. Evaluation and Model Selection
      4. Deployment and Monitoring
    6. Summary
  6. About the Authors

Product information

  • Title: Scaling Graph Learning for the Enterprise
  • Author(s): Ahmed Menshawy, Sameh Mohamed, Maraim Rizk Masoud
  • Release date: August 2025
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781098146061