Graph representation learning


Date Time Topic Presenter Location Details
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
Application, 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
Application, 20min
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

  1. Stanford CS224W: Machine Learning with Graphs, [link]
  2. ICLR 2021 Keynote – “Geometric Deep Learning: The Erlangen Programme of ML” – M Bronstein, [link]
  3. Graph Representation Learning: William L. Hamilton – 2021 McGill AI Learnathon, [link]
  4. Graph Neural Networks with Learnable Structural and Positional Representation, [link]
  5. Theoretical Foundations of Graph Neural Networks [pdf]
  6. Graph Representation Learning for Algorithmic Reasoning [pdf]
  7. A Gentle Introduction to Graph Neural Networks [link]


  1. 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.
  2. 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.
  3. Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584.
  4. 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.
  5. 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.
  6. Li, M. M., Huang, K., & Zitnik, M. (2022). Graph representation learning in biomedicine and healthcare. Nature Biomedical Engineering, 1-17.



  1. Jure Leskovec, Associate Professor of Computer Science at Stanford University, Google Scholar
  2. Thomas Kipf, Senior Research Scientist, Google Brain, Google Scholar
  3. William L. Hamilton, Assistant Professor of Computer Science, McGill University and Mila, Google Scholar
  4. Petar Veličković, Staff Research Scientist, DeepMind, Google Scholar
  5. Michael Bronstein, DeepMind Professor of AI, Google Scholar



  1. Graph representation learning

2. Deep Learning on Graphs

3. Graph Neural Networks