N. Kipf, P. Bloem, R. van den Berg, I. Titov, and M. Welling, “Modeling relational data with graph convolutional networks,” in ESWC 2018 MoNet (Monti+ 2017) F. Monti, D. Boscaini, J. Masci, E. Rodola, J. Svoboda, and M. M. Bronstein, “Geometric deep learning on graphs and manifolds using mixture model cnns,” CVPR 2017, pp. 5425–5434, 2017. GraphSAGE (Hamilton+ 2017) Hamilton, William L., et al. “Inductive Representation Learning on Large Graphs.” Advances in Neural Information Processing Systems, 2017, pp. 1024– 1034. DiffPool (Ying+ 2018) Ying, Zhitao, et al. “Hierarchical Graph Representation Learning with Differentiable Pooling.” NIPS 2018: The 32nd Annual Conference on Neural Information Processing Systems, 2018, pp. 4805–4815. NRI (Kipf+ 2018) Kipf, Thomas, et al. “Neural Relational Inference for Interacting Systems.” ICML 2018: Thirty-Fifth International Conference on Machine Learning, 2018, pp. 2688–2697. GIN (Xu+2019) Xu, Keyulu, et al. “How Powerful Are Graph Neural Networks.” ICLR 2019 : 7th International Conference on Learning Representations, 2019.