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
Practice: A Simple GNN Layer with NumPy
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- Semi-Supervised Classification with Graph Convolutional Networks, Thomas N. Kipf and Max Welling, 2017 International Conference on Learning Representations (ICLR 2017) DOI: 10.48550/arXiv.1609.02907 - This foundational paper introduced the Graph Convolutional Network (GCN) architecture, whose core layer, including the symmetric normalization and message passing scheme, is implemented in this section.
- Graph Representation Learning, William L. Hamilton, 2020 Synthesis Lectures on Artificial Intelligence and Machine Learning, Vol. 14 (Morgan & Claypool Publishers) DOI: 10.2200/S01045ED1V01Y202009AIM046 - Provides a comprehensive theoretical foundation for graph representation learning, offering detailed discussions on message passing neural networks and various GNN architectures, contextualizing the GCN layer implemented.
torch_geometric.nn.conv.GCNConv, PyTorch Geometric Developers, 2024 - Official documentation for the Graph Convolutional Network (GCN) layer implementation within the PyTorch Geometric library, illustrating how the core logic translates to a high-performance deep learning framework.- A Comprehensive Survey on Graph Neural Networks, Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu, 2020 IEEE Transactions on Neural Networks and Learning Systems, Vol. 32 (IEEE) DOI: 10.1109/TNNLS.2020.2978386 - This survey offers a broad overview of Graph Neural Networks, categorizing different architectures and detailing the general message passing framework that underlies many GNNs, including the GCN implemented here.
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