[Deep Graph Learning] 4.3 Recap on GNN sampling methods

#DGL #GCN #GNN 📚🔗 The CLEAN summary map of the DGL videos 4.1 to 4.6 can be found at: https://drive.google.com/file/d/1oDmri4iCqD6vE7iJIEgW5ghnMqW16V7s/view?usp=sharing 📚🔗 The ANNOTATED summary map of the DGL videos 4.1 to 4.6 can be found at: https://drive.google.com/file/d/1Zrdj8s4AdNLXYOFSygvDH6OfNHcqTktE/view?usp=sharing 👉 Reading material: 1. FastGCN. Chen, Jie, Tengfei Ma, and Cao Xiao. "Fastgcn: fast learning with graph convolutional networks via importance sampling." arXiv preprint arXiv:1801.10247 (2018). Link: https://arxiv.org/abs/1801.10247 2. GraphSage. Hamilton, Will, Zhitao Ying, and Jure Leskovec. "Inductive representation learning on large graphs." Advances in neural information processing systems 30 (2017). Link: https://proceedings.neurips.cc/paper_files/paper/2017/hash/5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html 3. GraphSaint. Zeng, Hanqing, et al. "Graphsaint: Graph sampling based inductive learning method." arXiv preprint arXiv:1907.04931 (2019). Link: https://arxiv.org/abs/1907.04931 4. ClusterGCN. Chiang, Wei-Lin, et al. "Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks." Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 2019. Link: https://dl.acm.org/doi/abs/10.1145/3292500.3330925 5. LADIES. Zou, Difan, et al. "Layer-dependent importance sampling for training deep and large graph convolutional networks." Advances in neural information processing systems 32 (2019). Link: https://proceedings.neurips.cc/paper/2019/hash/91ba4a4478a66bee9812b0804b6f9d1b-Abstract.html 6. DropGNN. Papp, Pál András, et al. "DropGNN: Random dropouts increase the expressiveness of graph neural networks." Advances in Neural Information Processing Systems 34 (2021): 21997-22009. Link: https://proceedings.neurips.cc/paper/2021/hash/b8b2926bd27d4307569ad119b6025f94-Abstract.html 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)