  We explore the generalization of scattering transforms from traditional
(e.g., image or audio) signals to graph data, analogous to the generalization
of ConvNets in geometric deep learning, and the utility of extracted graph
features in graph data analysis. In particular, we focus on the capacity of
these features to retain informative variability and relations in the data
(e.g., between individual graphs, or in aggregate), while relating our
construction to previous theoretical results that establish the stability of
similar transforms to families of graph deformations. We demonstrate the
application the our geometric scattering features in graph classification of
social network data, and in data exploration of biochemistry data.
