Graph Machine Learning Essentials

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.

Vibrant Publishers is focused on presenting the best texts for learning about technology and business as well as books for test preparation. Categories include programming, operating systems and other texts focused on IT. In addition, a series of books helps professionals in their own disciplines learn the business skills needed in their professional growth.

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Graph Machine Learning Essentials delivers a practical and technically grounded introduction to modern Graph ML, effectively connecting foundational graph concepts with real-world AI implementation workflows.
-- Lucas Cabral,
AI Engineer & Data Scientist

Graph Machine Learning Essentials is a compact, practical guide for engineers who want a quick start in Graph ML, covering key methods, tasks, applications, and implementation pathways. It is a valuable handbook for navigating modern graph ML concepts and real-world pipelines.
-- Dymitr Nowicki,
Ph.D. in Computer Science and Applied Mathematics,
Selecton Technologies Inc.

A comprehensive and accessible introduction to the burgeoning field of graph machine learning (GML). Aimed at readers with a basic understanding of machine learning, the book expertly balances theory and practice, making it suitable for students, professionals, and researchers alike.

The book begins by introducing graphs as structures that model relationships between entities, highlighting their ubiquity in domains like social networks, biology, and finance. Pintu explains why traditional machine learning methods fall short for graph-structured data, setting the stage for specialized techniques like node embeddings and Graph Neural Networks (GNNs). Each chapter builds logically on the last, covering core tasks (node classification, edge prediction, graph classification), advanced architectures, and practical considerations like scalability and over-smoothing.

What sets the book apart is its practical focus. Pintu includes code snippets, programming assignments, and discussions on real-world applications—such as fraud detection, recommender systems, and drug discovery—to ensure readers can apply what they learn. The use of quizzes and examples further reinforces understanding, while appendices on machine learning basics and PyTorch Geometric make the book self-contained.

Graph Machine Learning Essentials is an invaluable guide for anyone looking to understand and apply GML, offering both the theoretical foundations and the practical tools needed to harness the power of graph-structured data.
-- Wilson Yeung,
Reviewer

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