"a beautiful article. There's code, there's math. There are many plotted examples using a variety of different plotting techniques (in total, representing a 2d array as data, or in black/white, or with a terrible 'jet' colormap, or as a 3d terrain."
Graph neural networks are intimately related to partial differential equations governing information diffusion on graphs. Thinking of GNNs as PDEs leads to a new broad class of graph ML methods.
Z. Long, Y. Lu, and B. Dong. (2018)cite arxiv:1812.04426Comment: 16 pages, 15 figures. arXiv admin note: substantial text overlap with arXiv:1710.09668.
Z. Long, Y. Lu, and B. Dong. (2018)cite arxiv:1812.04426Comment: 16 pages, 15 figures. arXiv admin note: substantial text overlap with arXiv:1710.09668.