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.
Feng Gao (Michigan State University)
Guy Wolf (Université de Montréal)
Matthew Hirn (Michigan State University)
Related Events (a corresponding poster, oral, or spotlight)
2019 Poster: Geometric Scattering for Graph Data Analysis »
Thu Jun 13th 06:30 -- 09:00 PM Room Pacific Ballroom