[Deep Graph Learning] 6.5 Supervised conditional generation on graphs
#DGL #GCN #GNN
📚🔗 The CLEAN summary map of the DGL videos 6.1 to 6.7 can be found at: https://drive.google.com/file/d/12Vki...
📚🔗 The ANNOTATED summary map of the DGL videos 6.1 to 6.7 can be found at: https://drive.google.com/file/d/16HON...
👉 Reading material:
1. Supervised generative tasks: Bessadok, A., Mahjoub, M. A., & Rekik, I. (2022). Graph neural networks in network neuroscience. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5), 5833-5848.
2.Taxonomy: Guo, X., & Zhao, L. (2022). A systematic survey on deep generative models for graph generation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5), 5370-5390.
3. GraphVAE: Simonovsky, M., & Komodakis, N. (2018). Graphvae: Towards generation of small graphs using variational autoencoders. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018
4. DAG-to-DAG: Kaluza, M. C. D. P., Amizadeh, S., & Yu, R. (2018). A neural framework for learning DAG to DAG translation. In NeurIPS’2018 Workshop.
5. Graph U-Net: Gao, H., & Ji, S. (2019, May). Graph U-Nets. In international conference on machine learning (pp. 2083-2092). PMLR.
6. Evaluation: Thompson, R., Knyazev, B., Ghalebi, E., Kim, J., & Taylor, G. W. (2022). On evaluation metrics for graph generative models. arXiv preprint arXiv:2201.09871. MSc thesis.
7. NED-VAE: Guo, X., Zhao, L., Qin, Z., Wu, L., Shehu, A., & Ye, Y. (2020, August). Interpretable deep graph generation with node-edge co-disentanglement.
8. https://wandb.ai/syllogismos/machine-...
Unlock the world of Deep Graph Learning with our new video series!
🚀 Dive into the mathematical foundations of graph neural networks using an intuitive approach and the power of linear algebra.
🙏 Special thanks to Simon Prince, Alex Fornito, Andrew Zalesky, Edward Bullmore, Jure Leskovec and all those who shared their passion about graphs and deep learning.
Textbooks:
• Simon Prince; Understanding Deep Learning (2023); https://github.com/udlbook/
• Bullmore, Edward T., Fornito, Alex, and Zalesky, Andrew; Fundamentals of Brain Network Analysis-Academic Press, Elsevier (2016)