Introduction to Graph Neural Networks
Chapter 1: Foundations of Graph-Based Learning
What is Graph Data?
Limitations of Standard Neural Networks on Graphs
Common Graph Machine Learning Tasks
Representing Graphs: Adjacency and Feature Matrices
Graph Properties and Measurements
Introduction to the NetworkX Library
Hands-on: Loading and Inspecting a Graph Dataset
Chapter 2: The Message Passing Mechanism
The Neighborhood Aggregation Idea
A General GNN Layer: Aggregate and Update
Common Aggregation Functions
Update Functions and Non-Linearities
Permutation Invariance and Equivariance
Stacking Layers to Form a Deep GNN
Practice: A Simple GNN Layer with NumPy
Chapter 3: Foundational GNN Architectures
Graph Convolutional Networks (GCN)
A Spatial Interpretation of Graph Convolutions
GraphSAGE: Sampling and Aggregating Neighborhoods
Inductive Learning with GraphSAGE
Graph Attention Networks (GAT)
The Attention Mechanism in GATs
Comparing GCN, GraphSAGE, and GAT
Hands-on: Implementing a GCN from Scratch
Chapter 4: Training GNN Models
Setting up a GNN for Node Classification
Loss Functions for Graph Tasks
The Training Loop for GNNs
Data Splitting in Graphs: Transductive vs. Inductive
Evaluation Metrics for Node Classification
Overfitting and Regularization in GNNs
Practice: Training and Evaluating your GCN
Chapter 5: GNN Implementation with PyTorch Geometric
Introduction to PyTorch Geometric (PyG)
The PyG Data Object
Working with PyG Datasets
Building Models with PyG GNN Layers
Mini-Batching for Large Graphs
A Complete Training Script in PyG
Hands-on: Node Classification on the Cora Dataset with PyG
Representing Graphs: Adjacency and Feature Matrices
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- Introduction to Graph Theory, Douglas B. West, 2001 (Prentice Hall) - A classic textbook covering fundamental concepts of graph theory, including adjacency matrices and other graph representations.
- Graph Representation Learning, William L. Hamilton, 2020 Synthesis Lectures on Artificial Intelligence and Machine Learning, Vol. 14 (Morgan and Claypool) DOI: 10.2200/S00998ED1V01Y202003AIM007 - An authoritative book focusing on graph representation learning, beginning with the numerical encoding of graph structures and node features.
- Dive into Deep Learning, Aston Zhang, Zachary C. Lipton, Mu Li, Alex Smola, and others, 2024 (Cambridge University Press) - An accessible online textbook, with a chapter dedicated to Graph Neural Networks that explains how graphs are numerically represented as input.
- A Comprehensive Survey on Graph Neural Networks, Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, 2020 IEEE Transactions on Neural Networks and Learning Systems, Vol. 32 (IEEE) DOI: 10.1109/TNNLS.2020.2970760 - A widely cited survey providing a broad overview of Graph Neural Networks, including the foundational methods for representing graphs as input data.