On Dynamic Graph and Edge Convolution Operations
This case study explores `EdgeConv` and `DGCNN` to build an intuitive understanding of their working principles and implementation. It does so by using the ModelNet10 dataset for benchmarking on a 3D classification task and examines the mathematics behind the techniques. The article concludes with a reflection on the method’s capabilities and limitations, as well as some potential applications in cosmology and the arts.
