Abstract
A number of problems can be formulated as prediction on graph-structured
data. In this work, we generalize the convolution operator from regular grids
to arbitrary graphs while avoiding the spectral domain, which allows us to
handle graphs of varying size and connectivity. To move beyond a simple
diffusion, filter weights are conditioned on the specific edge labels in the
neighborhood of a vertex. Together with the proper choice of graph coarsening,
we explore constructing deep neural networks for graph classification. In
particular, we demonstrate the generality of our formulation in point cloud
classification, where we set the new state of the art, and on a graph
classification dataset, where we outperform other deep learning approaches. The
source code is available at https://github.com/mys007/ecc
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