We go from Message Passing GNN (MPGNN) to TOPOLOGICAL Message Passing on CW Networks: Lifting a Graph to a higher topological space allows for high-dimensional interactions (greater than 2) given our higher-dim topological spaces. Computational Graph Neural Networks increase its complexities and n-body interactions (eg chemistry, pharma, molecular design). We go from Message Passing GNN (MPGNN) to TOPOLOGICAL Message Passing on CW Networks. First part of this video is here: https://www.youtube.com/watch?v=Xiy_bD1CHro All credits go to: Michael Bronstein, "Beyond Message Passing, a Physics-Inspired Paradigm for Graph Neural Networks", The Gradient, 2022. P. Veličković, Message passing all the way up (2022) arXiv:2202.11097. J. Zhu et al., Beyond homophily in graph neural networks: Current limitations and effective designs (2020), NeurIPS. P. Veličković et al., Graph Attention Networks (2018) ICLR. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges by Michael M. Bronstein et al. https://arxiv.org/abs/2104.13478 Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks Cristian Bodnar, Fabrizio Frasca, Yu Guang Wang, Nina Otter, Guido Montúfar, Pietro Liò, Michael Bronstein https://arxiv.org/abs/2103.03212 Just discovered a great explanatory video on topic of CWL by https://www.youtube.com/channel/UCVt9rN2LzKzNwJG-VXvZGkA https://www.youtube.com/watch?v=wACDSoDNTfE #ai #topologicalspace #deeplearning #topology