|2023-08-18||2h||Background, Representation, Learning and Application – Homogeneous graph (Part 1)||Hao Cheng, Dr. Ma||Lincoln 350 Conf Rm|
|2023-08-21||1h||Background, Representation, Learning and Application – Homogeneous graph (Part 2)||Hao Cheng, Yi Jiang||Lincoln 350 Conf Rm|
|2023-09-05||1h||Background, Representation, Learning and Application – Homogeneous graph (Part 3)||Hao Cheng, Yi Jiang||PaRC 3001|
|2023-09-25||2h||Framework – Machine Learning Foundation and Self-supervised Learning||Yi Jiang, Hao Cheng, Anjun Ma||PaRC 3001||Graph representation learning callback (extra meeting, application), 20 min
Machine learning foundation, supervised/unsupervised learning, loss function, 30 min
Self-supervised learning, 1h15min
|2023-10-02||1.5h||Representation, Learning and Application – Heterogeneous graph||Hao Cheng, Xiaoying Wang||PaRC 3001||Representation, 10 min
Learning, 1 h
Application, 20 min
|2023-10-16||1.5h||Learning and Application – Graph Transformer||Yi Jiang||PaRC 3001||Transformer (video), 30min
Homogeneous graph transformer, 30min
Heterogeneous graph transformer, 30min
|2023-10-30||1.5h||Framework – Graph generative model||Hao Cheng, Yi Jiang||PaRC 3001||Callback and background, 30min
Generative model, 30min
|2023-11-13||2h||Representation, Learning and Application – Hypergraph and Line graph||Yi Jiang, Hao Cheng||PaRC 3001||Callback, 10min
Hypergraph and Line graph (video), 1h30min
|2023-11-27||2h||Framework – Transfer Learning and Foundation models||Hao Cheng||PaRC 3001||Callback transformer, 10min
Transfer learning models, 40min
Foundation models, 40min
|2023-12-13||2h||Representation, Learning and Application – Dynamic graph and De Bruijn Graph||Yi Jiang, Hao Cheng||PaRC 3001|
|2023-12-27||1.5h||Framework – Reinforcement learning||Hao Cheng||PaRC 3001|
|2024-01-13||2h||Casual – Casual discovery and Casual inference||Yi Jiang||PaRC 3001|
Related materials can be found from the Dropbox folder: BMBL Shared\BMBL-Resources\Graph representation learning.
Talks, courses and blogs
- Stanford CS224W: Machine Learning with Graphs, [link]
- ICLR 2021 Keynote – “Geometric Deep Learning: The Erlangen Programme of ML” – M Bronstein, [link]
- Graph Representation Learning: William L. Hamilton – 2021 McGill AI Learnathon, [link]
- Graph Neural Networks with Learnable Structural and Positional Representation, [link]
- Theoretical Foundations of Graph Neural Networks [pdf]
- Graph Representation Learning for Algorithmic Reasoning [pdf]
- A Gentle Introduction to Graph Neural Networks [link]
- Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., … & Pascanu, R. (2018). Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261.
- Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Philip, S. Y. (2020). A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, 32(1), 4-24.
- Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584.
- Xie, Y., Xu, Z., Zhang, J., Wang, Z., & Ji, S. (2022). Self-supervised learning of graph neural networks: A unified review. IEEE transactions on pattern analysis and machine intelligence.
- Yi, H. C., You, Z. H., Huang, D. S., & Kwoh, C. K. (2022). Graph representation learning in bioinformatics: trends, methods and applications. Briefings in Bioinformatics, 23(1), bbab340.
- Li, M. M., Huang, K., & Zitnik, M. (2022). Graph representation learning in biomedicine and healthcare. Nature Biomedical Engineering, 1-17.
- Jure Leskovec, Associate Professor of Computer Science at Stanford University, Google Scholar
- Thomas Kipf, Senior Research Scientist, Google Brain, Google Scholar
- William L. Hamilton, Assistant Professor of Computer Science, McGill University and Mila, Google Scholar
- Petar Veličković, Staff Research Scientist, DeepMind, Google Scholar
- Michael Bronstein, DeepMind Professor of AI, Google Scholar
- Graph representation learning
2. Deep Learning on Graphs
3. Graph Neural Networks