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.