Using Graph CNNs in Keras

GraphCNNs recently got interesting with some easy to use keras implementations. The basic idea of a graph based neural network is that not all data comes in traditional table form. Instead some data comes in well, graph form. Other relevant forms are spherical data or any other type of manifold considered in geometric deep learning. So what does graph data look like if not like a table? Here’s an example: Let’s put some meaning into those variables, and no I’m not gonna use a “citation network” example which would be the default for graph based neural networks. While easy to […]

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