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Graph Machine Learning Essentials
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Graph Machine Learning Essentials
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This book will be shipped on August 08, 2026.
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What if the most important information in your data lies not in individual rows and columns, but in the connections between them? Graph machine learning helps uncover patterns hidden in these relationships.
Graph Machine Learning Essentials is a practical and accessible guide to understanding how machine learning works with graph-structured data, where entities are connected through relationships.
Designed for software engineers, ML engineers, data scientists, research scholars, professionals, cybersecurity analysts, and students, the book introduces graph machine learning in a clear and structured way. It begins with the fundamentals of graph theory and moves into core graph learning tasks such as node classification, edge prediction, and graph classification. Readers learn how graphs are represented in data structures, how node and edge embeddings work, and why traditional machine learning approaches do not directly apply to graph data.
The book gradually builds toward graph neural networks, message passing, and advanced GNN architectures while explaining practical challenges such as graph construction, scalability, oversmoothing, and over-squashing. Concepts are connected to real-world applications across domains such as recommender systems, fraud detection, cybersecurity, bioinformatics, transportation networks, and knowledge graphs.
The book includes two helpful appendices—one reviewing essential machine learning concepts and the other introducing PyTorch Geometric to help readers get started quickly.
After reading this book, you will be able to:
- Understand key graph machine learning concepts and terminology
- Implement graph neural networks using PyTorch Geometric
- Work on real-world graph learning problems across industries
- Handle practical challenges such as large graphs and oversmoothing
Pages: 205 pages
Paperback (ISBN): 9781636517032
eBook (ISBN): 9781636517049
Hardback (Color): 9781636517056
Category: Business & Economics
Author: Sona Murgai, Vibrant Publishers
1 Introduction to Graph Machine Learning
1.1: Introduction to Graphs
1.2: The Power of Graphs: Real-world Examples
of Graphs
1.3: Why Traditional ML Methods Do Not Work
Directly for Graphs
Quiz
2 Common Tasks on Graph Data
2.1: Representing Graphs in Data Structures
2.2: Node Classification
2.3: Edge Prediction
2.4: Graph Classification
2.5: Unsupervised Graph Tasks
Quiz
3 Node Embedding Techniques
3.1: Node Embedding Techniques
3.2: Random Walk-based Node Embedding
3.3: Edge Embedding
3.4: Practical Insights on Using Graph Embedding
Quiz
4 Basics of Graph Neural Networks
4.1: Multi-layer Perceptron - A Quick Recap
4.2: Introduction to Graph Neural Network
4.3: Self-loop-based Methods
4.4: GNN for Node Classification
Quiz
5 GNN for Edge Prediction and Graph Classification
5.1: GNN for Edge Prediction
5.2: GNN for Graph Classification
Quiz
6 Advanced GNN Architectures
6.1: Advanced Aggregation Methods
6.2: Advanced Update Function
Quiz
7 Applications of Graph Machine Learning
7.1: Social Network Analysis
7.2: Fraud Detection and Cybersecurity
7.3: Recommender Systems
7.4: Bioinformatics: Drug Discovery and Protein
Interaction Networks
7.5: Optimizing Transportation Networks
7.6: Powering Search and Discovery with Knowledge
Graphs
Quiz
8 Practical Considerations in Graph ML
8.1: Graph Construction: From Data to Graph
8.2: Scalability: Training GNNs on Large Graphs
8.3: Over-squashing: The Information Bottleneck
8.4: Choosing Model Depth and the Oversmoothing
Problem
Quiz
Appendix A: A Brief Recap of Machine Learning
A.1: Linear Regression
A.2: Introduction to Classification
A.3: The Perceptron: A Different View of Linear
Classification
A.4: Multi-layer Perceptrons: Learning Nonlinear
Functions
Appendix B: Introduction to PyTorch Geometric (PyG)
B.1: Installation
B.2: Creating a Basic Graph in PyG
B.3: Basic Operations and Attributes Of A Graph
B.4: Loading Existing Datasets
B.5: Accelerating with a GPU
B.6: Defining a GNN Architecture
B.7: Training the GNN
Pintu Kumar is a Ph.D. scholar at IIT Bombay specializing in graph machine learning. A PMRF fellow and Silver Medalist in Mathematics, he focuses on research, teaching, and making complex ideas accessible.
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