Deep Learning

Deep Learning by Bishop [link]

A Gentle Introduction to Graph Neural Networks -- a high-level introduction to graph neural nets and visualizations.

Xie, Yaochen, et al. "Self-supervised learning of graph neural networks: A unified review." IEEE Transactions on Pattern Analysis and Machine Intelligence (2022).

Wu, Zonghan, et al. "A comprehensive survey on graph neural networks." IEEE transactions on neural networks and learning systems 32.1 (2020): 4-24.

Zhou, Jie, et al. "Graph neural networks: A review of methods and applications." AI Open 1 (2020): 57-81.

Zhang, Ziwei, Peng Cui, and Wenwu Zhu. "Deep learning on graphs: A survey." IEEE Transactions on Knowledge and Data Engineering (2020).

Wu, Zhenqin, et al. "MoleculeNet: a benchmark for molecular machine learning." Chemical science 9.2 (2018): 513-530.

Xu, Keyulu, et al. "How powerful are graph neural networks?." arXiv preprint arXiv:1810.00826 (2018).

Gilmer, Justin, et al. "Neural message passing for quantum chemistry." International conference on machine learning. PMLR, 2017.

Kipf, Thomas N., and Max Welling. "Semi-supervised classification with graph convolutional networks." arXiv preprint arXiv:1609.02907 (2016).