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
Limitations of Standard Neural Networks on Graphs
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- The Graph Neural Network Model, Franco Scarselli, Marco Gori, Ah Chung Tsoi, Mohamed Kamel, Luca Sperduti, 2009 IEEE Transactions on Neural Networks, Vol. 20 (IEEE) DOI: 10.1109/TNN.2008.2005605 - Presents one of the earliest formal definitions of Graph Neural Networks and clearly outlines the necessity for models capable of processing graph-structured data by discussing the limitations of traditional neural networks.
- Geometric Deep Learning: Going Beyond Euclidean Data, Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, Pierre Vandergheynst, 2017 IEEE Signal Processing Magazine, Vol. 34 (IEEE) DOI: 10.1109/MSP.2017.2693418 - Introduces the concept of Geometric Deep Learning, providing a theoretical framework that highlights why traditional deep learning models designed for Euclidean data fail on non-Euclidean structures such as graphs.
- Graph Neural Networks: A Review of Methods and Applications, Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, Maosong Sun, 2020 AI Open, Vol. 1 (KeAi Publishing) DOI: 10.1016/j.aiopen.2021.04.001 - A widely cited comprehensive survey that provides an extensive overview of Graph Neural Networks, including a clear explanation of why traditional deep learning models struggle with graph data.
- Graph Neural Networks: Foundations, Frontiers, and Applications, Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao, Le Song, 2022 (Springer) DOI: 10.1007/978-3-031-01588-5 - A foundational textbook that details the principles of graph neural networks, starting with the inherent challenges of applying standard deep learning techniques to graph-structured data.