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
What is Graph Data?
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- Graph Theory, Reinhard Diestel, 2017 Vol. 173 (Springer-Verlag) DOI: 10.1007/978-3-662-53622-3 - A classic and authoritative textbook that provides a formal treatment of graph theory, including definitions of graphs, nodes, edges, and various graph properties.
- Graph Representation Learning, William L. Hamilton, 2020 Synthesis Lectures on Artificial Intelligence and Machine Learning, Vol. Vol. 14, No. 3 (Morgan & Claypool Publishers) DOI: 10.2200/S01045ED1V01Y202009AIM046 - An essential resource for understanding how graph data is structured and represented for machine learning tasks, covering node, edge, and global features, and different graph types.
- Graph Neural Networks: A Review of Methods and Applications, Jie Zhou, Ganqu Cui, Zhengyu Chen, Ming Ding, Shuai Sun, Xuan Wang, and Lifang He, 2021 AI Open, Vol. 1 (Elsevier) DOI: 10.1016/j.aiopen.2021.05.001 - A widely cited survey article that introduces Graph Neural Networks, beginning with foundational graph definitions and their relevance to machine learning applications.
- Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges, Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković, 2021 (MIT Press) - A comprehensive textbook on geometric deep learning, including detailed descriptions of graph data, its properties, and its role as a fundamental structure for deep learning on non-Euclidean domains.