Master next-generation deep learning techniques for handling social networks, molecular structures, and knowledge graphs from fundamentals to systematic understanding
Series Overview
This series is a practical educational content consisting of 5 chapters that progressively teaches the theory and implementation of Graph Neural Networks (GNN) from fundamentals.
Graph Neural Networks (GNN) are deep learning methods for graph-structured data. They learn features from relational data represented by nodes and edges, such as social networks, molecular structures, transportation networks, and knowledge graphs. The application of spectral graph theory through Graph Convolutional Networks (GCN), aggregation of neighborhood information through message passing frameworks, and learning of importance through Graph Attention Networks (GAT) - these technologies are bringing innovation across a wide range of fields including drug discovery, recommendation systems, transportation optimization, and knowledge reasoning. You will understand and be able to implement the foundational graph learning technologies being practically applied by companies like Google, Facebook, and Amazon. We provide systematic knowledge from graph theory fundamentals to Graph Transformers.
Features:
- From Theory to Implementation: Systematic learning from graph theory fundamentals to the latest Graph Transformers
- Implementation Focus: Over 40 executable PyTorch/PyG/DGL code examples and practical techniques
- Intuitive Understanding: Understanding principles through graph visualization and message passing operation visualization
- Latest Technology Standards: Implementation using PyTorch Geometric and DGL (Deep Graph Library)
- Practical Applications: Application to practical tasks such as node classification, graph classification, link prediction, and drug discovery
Total Learning Time: 120-150 minutes (including code execution and exercises)
How to Proceed with Learning
Recommended Learning Sequence
For Beginners (No GNN knowledge):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5 (All chapters recommended)
- Duration: 120-150 minutes
For Intermediate Learners (Experience with graph theory):
- Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
- Duration: 90-110 minutes
For Specific Topic Enhancement:
- Graph Theory: Chapter 1 (Focused study)
- GCN Theory: Chapter 2 (Focused study)
- Message Passing: Chapter 3 (Focused study)
- GAT/Graph Transformer: Chapter 4 (Focused study)
- Duration: 25-30 minutes per chapter
Chapter Details
Chapter 1: Fundamentals of Graphs and Graph Representation Learning
Difficulty: Advanced
Reading Time: 25-30 minutes
Code Examples: 8
Learning Content
- Graph Theory Fundamentals - Nodes, edges, adjacency matrix, degree matrix
- Types of Graphs - Directed graphs, undirected graphs, weighted graphs, heterogeneous graphs
- Graph Representation Methods - Adjacency matrix, adjacency list, edge list
- Node Embeddings - DeepWalk, Node2Vec, objectives of graph representation learning
- Graph Visualization - NetworkX, graph construction with PyTorch
Learning Objectives
- Understand basic concepts of graph theory
- Explain mathematical representations of graphs
- Construct adjacency matrices and degree matrices
- Understand the role of node embeddings
- Visualize graphs using NetworkX
Chapter 2: Graph Convolutional Networks (GCN)
Difficulty: Advanced
Reading Time: 25-30 minutes
Code Examples: 8
Learning Content
- Spectral Graph Theory - Laplacian matrix, eigenvalue decomposition, graph Fourier transform
- GCN Principles - Extension of convolution to graphs, aggregation of neighborhood information
- GCN Layer Formulation - Symmetric normalization, activation functions
- Implementation with PyTorch Geometric - GCNConv, data preparation
- Application to Node Classification - Cora/CiteSeer datasets, paper classification
Learning Objectives
- Understand fundamentals of spectral graph theory
- Explain GCN convolution operations
- Understand the role of Laplacian matrices
- Implement GCN using PyTorch Geometric
- Solve node classification tasks
Chapter 3: Message Passing and GNN
Difficulty: Advanced
Reading Time: 25-30 minutes
Code Examples: 9
Learning Content
- Message Passing Framework - Message, Aggregate, Update
- GraphSAGE - Sampling and aggregation, scalable learning
- Graph Isomorphism Network (GIN) - Theoretical guarantees of expressive power
- Using Edge Features - Edge convolution, learning relationships
- Over-smoothing Problem - Challenges of deep GNNs and solutions
Learning Objectives
- Understand the message passing framework
- Explain GraphSAGE aggregation methods
- Understand the strong expressive power of GIN
- Implement GNN utilizing edge features
- Address the over-smoothing problem
Chapter 4: Graph Attention Networks (GAT)
Difficulty: Advanced
Reading Time: 30-35 minutes
Code Examples: 8
Learning Content
- Review of Attention Mechanisms - Self-attention, Query-Key-Value
- GAT Principles - Attention on graphs, learning importance of neighbors
- Multi-head Attention - Multiple attention heads, improved expressive power
- Graph Transformer - Application of Transformers to graphs
- Positional Encoding - Laplacian eigenvectors, injection of structural information
Learning Objectives
- Understand GAT attention mechanisms
- Explain calculation methods for attention coefficients
- Understand benefits of multi-head attention
- Explain Graph Transformer mechanisms
- Implement GAT using PyTorch Geometric
Chapter 5: Applications of GNN
Difficulty: Advanced
Reading Time: 25-30 minutes
Code Examples: 7
Learning Content
- Node Classification - Paper classification, social network analysis
- Graph Classification - Molecular property prediction, protein function prediction
- Link Prediction - Recommendation systems, knowledge graph completion
- Applications in Drug Discovery - Molecular generation, drug-target interaction prediction
- Knowledge Graphs and GNN - Entity embeddings, relational reasoning
Learning Objectives
- Understand problem settings for each task
- Implement node classification models
- Utilize pooling methods in graph classification
- Understand evaluation metrics for link prediction
- Understand applications to drug discovery and knowledge graphs
Overall Learning Outcomes
Upon completing this series, you will acquire the following skills and knowledge:
Knowledge Level (Understanding)
- Explain the theoretical foundations of graph theory and GNN
- Understand the mechanisms of GCN, GraphSAGE, GIN, and GAT
- Explain the message passing framework
- Understand the application of attention mechanisms to graphs
- Explain how to choose between different GNN architectures
Practical Skills (Doing)
- Implement GNN using PyTorch Geometric/DGL
- Solve node classification, graph classification, and link prediction
- Manipulate and visualize graph data using NetworkX
- Implement attention mechanisms with GAT
- Apply GNN to drug discovery and recommendation systems
Application Ability (Applying)
- Select appropriate GNN architecture according to tasks
- Utilize graph data in practical work
- Address the over-smoothing problem
- Understand and utilize the latest Graph Transformer technologies
Prerequisites
To effectively learn this series, it is desirable to have the following knowledge:
Required (Must Have)
- Python Fundamentals: Variables, functions, classes, loops, conditional statements
- NumPy Fundamentals: Array operations, matrix operations, basic linear algebra
- Deep Learning Fundamentals: Neural networks, backpropagation, gradient descent
- PyTorch Fundamentals: Tensor operations, nn.Module, Dataset and DataLoader
- Linear Algebra Fundamentals: Matrix operations, eigenvalues/eigenvectors, diagonalization
- Graph Theory Fundamentals: Nodes, edges, adjacency matrix (recommended)
Recommended (Nice to Have)
- CNN Fundamentals: Concepts of convolution operations (for understanding GCN)
- Attention Mechanisms: Self-attention, Transformer (for understanding GAT)
- Optimization Algorithms: Adam, learning rate scheduling
- NetworkX: Basics of graph data manipulation library
- GPU Environment: Basic understanding of CUDA
Recommended prior learning:
Technologies and Tools Used
Main Libraries
- PyTorch 2.0+ - Deep learning framework
- PyTorch Geometric 2.3+ - Graph neural network library
- DGL (Deep Graph Library) 1.1+ - Graph deep learning framework
- NetworkX 3.1+ - Graph construction, manipulation, visualization
- NumPy 1.24+ - Numerical computation
- Matplotlib 3.7+ - Visualization
- scikit-learn 1.3+ - Evaluation metrics, preprocessing
- RDKit 2023+ - Molecular data processing (drug discovery applications)
Development Environment
- Python 3.8+ - Programming language
- Jupyter Notebook / Lab - Interactive development environment
- Google Colab - GPU environment (free to use)
- CUDA 11.8+ / cuDNN - GPU acceleration (recommended)
Datasets
- Cora / CiteSeer - Paper citation networks (node classification)
- PubMed - Medical paper citation network
- MUTAG / PROTEINS - Molecular datasets (graph classification)
- Zachary's Karate Club - Social network (visualization and learning)
- OGB (Open Graph Benchmark) - Graph learning benchmarks
Let's Get Started!
Are you ready? Start with Chapter 1 and master Graph Neural Network technologies!
Chapter 1: Fundamentals of Graphs and Graph Representation Learning →
Next Steps
After completing this series, we recommend proceeding to the following topics:
In-Depth Learning
- Graph Transformers: Full-scale application of Transformers to graphs
- Temporal Graph Networks: Learning time-evolving graphs
- Heterogeneous Graphs: Heterogeneous graph neural networks
- Graph Generation: Graph generation models, molecular generation
Related Series
- - Knowledge reasoning, relation extraction
- - Graph-based recommendation, collaborative filtering
- - Molecular generation, drug-target interaction prediction
Practical Projects
- Paper recommendation system - Citation networks and GNN
- Molecular property prediction - Toxicity and activity prediction through graph classification
- Social network analysis - Community detection, influence prediction
- Knowledge graph completion - Knowledge reasoning through link prediction
Update History
- 2025-10-21: v1.0 First edition released
Your journey into Graph Neural Networks learning begins here!