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Laplacian Matrix, Fourier Transform and Convolution on Graphs

Convolutional neural networks (CNNs) have gain great success in many fields in machine learning. It is however not so obvious how convolution can be performed on non-Euclidean structures such as graphs. Starting from a simple diffusion model, we examine different concepts, namely, the graph Laplacian matrix and the Fourier transform, and show the relations between them. The convolution on graphs can be naturally defined once these relations become clear.

22.11.2018 - 10:15
B 016