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Course Materials
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{% include image.html url="/_images/Gnn_foundation.jpg" width=135 align="right" %} {% include image.html url="/_images/dl.jpg" width=135 align="right" %} {% include image.html url="/_images/grl.jpg" width=135 align="right" %} {% include image.html url="/_images/dl_on_graphs.jpg" width=135 align="right" %}

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]

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]

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