further reading
Chapter 1
Chen, F., Wang, Y-C., Wang, B., and Kuo, C-C. Jay. (2020). Graph representation learning: A survey. APSIPA Transactions on Signal and Information Processing, 9, e15.
Elinas, P. (2019, June 5). Knowing your neighbours: Machine learning on graphs. https://mng.bz/1XBQ
Hua, C., Rabusseau, G., and Tang, J. (2022). High-order pooling for graph neural networks with tensor decomposition. Advances in Neural Information Processing Systems, 35, 6021-6033.
Liu, Z., and Zhou, J. (2020). Introduction to Graph Neural Networks, Morgan & Claypool.
Sanchez-Gonzalez, A., Heess, N., Springenberg, J. T., et al. (2018). Graph networks as learnable physics engines for inference and control. In An International Conference on Machine Learning (pp. 4470–4479). PMLR.
Chapter 2
DIMACS. (2011). Tenth DIMACS Implementation Challenge. Georgia Institute of Technology. https://sites.cc.gatech.edu/dimacs10/archive/clustering.shtml
Duong, C. T., Hoang, T. D., Dang, H. T. H., Nguyen, Q. V. H., and Aberer, K. (2019). On node features for graph neural networks. arXiv preprint arXiv:1911.08795.
Krebs, V. (2003, January). Divided we stand??? Orgnet.com. www.orgnet.com/divided1.html
Chapter 4
Cai, T., Shengjie L., Keyulu X., et al. (2021). GraphNorm: A principled approach to accelerating graph neural network training. In Proceedings of the 38th International Conference on Machine Learning (pp. 1204–1215). PMLR.