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Skewness and Kurtosis are similar, but different. Skewness is the degree of distortion from the symmetrical bell curve or the normal distribution. It measures the lack of symmetry in data distribution. It differentiates extreme values in one versus the other tail. A symmetrical distribution will have a skewness of 0. Kurtosis is all about the tails of ...


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One of the most popular ways of doing classification on graphs is through graph convolutional networks. By running a convolution over the nodes of a graph, the neural net is able to learn the local neighborhoods of the graph. The seminal example is probably the paper Semi-Supervised Classification with Graph Convolutional Networks by Kipf and Welling. https:...


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You either need to create a new figure before plotting by plt.figure(), or you need to use subplots. Otherwise, plt.show() will act on the last-created figure.


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That depends what you want to show and how much information fits in the graph. Typically you can think of: Simply using a different colour for every dataset, but 20 is probably too many and that will make the graph hard to read. Using boxplots (or violin plots) at every instant, where the boxplot represents the 20 values of the dataset.


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you can use some thing like DeepWalk to embed each node so it is straightforward to do any classification on that embedding as it is just a vector.


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I'm not sure if that's what you need but this library seems pretty similar to your problem: https://github.com/Accenture/AmpliGraph/


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