Geometric Scattering for Graph Data Analysis
This provides a new feature extraction method for graph data analysis, though it appears incremental as an adaptation of existing scattering transforms to graphs.
The authors generalized scattering transforms from traditional signals to graph data and demonstrated their utility in graph classification and data exploration tasks, achieving competitive performance on social network and biochemistry datasets.
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.