Course Materials

Books

1. Deep Learning on Graphs, Yao Ma, Jiliang Tang, Cambridge University Press, 2021 [link]
2. Graph Representation Learning, William L. Hamilton, Morgan & Claypool Publishers, 2020 [link]
3. Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville, MIT Press, 2016 [link]
4. Graph Neural Networks, Dr. Lingfei Wu, Dr. Peng Cui, Dr. Jian Pei, Dr. Liang Zhao, Springer Singapore, 2022 [link]

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Datasets

1. Open Graph Benchmarks: [link]
2. Network Data: [link]

Tools

1. Libraries
   • NetworkX:Network Analysis in Python [link]
   • Pytorch Geometric: Pytorch Based GNN Library [link]
   • Deep Graph Library: Pytorch and Tensorflow based GNN Library [link]
   • Spektral: Python Based GNN Library [link]

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Additional Course Material

• Additional Learning Material will be added as we move forward in the course
• This section is Temporary: Material will be updated soon