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
Overfitting and Regularization in GNNs
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- Graph Neural Networks: A Review of Methods and Applications, Jie Zhou, Ganqu Cui, Zhengyu Dai, Shuai Sun, Ling Shao, Jianxin Li, Yang You, Zenglin Xu, 2020 AI Open, Vol. 1 (Elsevier) DOI: 10.1016/j.aiopen.2020.11.001 - Provides a comprehensive overview of GNNs, including discussions on common challenges like overfitting and over-smoothing, and mentions various regularization techniques.
- DropEdge: Towards Deep Graph Convolutional Networks on Node Classification, Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang, 2020 International Conference on Learning Representations (ICLR 2020) (ICLR) DOI: 10.48550/arXiv.1911.08070 - Introduces DropEdge, a regularization technique specific to GNNs that randomly removes edges during training to prevent overfitting and improve generalization.
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting, Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov, 2014 Journal of Machine Learning Research (JMLR), Vol. 15 (JMLR) - The original paper introducing Dropout, a fundamental regularization technique widely used in deep learning, including GNNs.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A comprehensive textbook covering fundamental deep learning concepts, including detailed explanations of weight decay (L2 regularization) and early stopping.