Graph Fourier MMD for Signals on Graphs
Samuel Leone Aarthi Venkat Guillaume Huguet Alexander Tong Guy Wolf Smita Krishnaswamy
While numerous methods have been proposed for computing distances between
probability distributions in Euclidean space, relatively little attention has
been given to computing such distances for distributions on graphs. However,
there has been a marked increase in data that either lies on graph (such as
protein interaction networks) or can be modeled as a graph (single cell data),
particularly in the biomedical sciences. Thus, it becomes important to find
ways to compare signals defined on such graphs. Here, we propose Graph Fourier
MMD (GFMMD), a novel distance between distributions and signals on graphs.
GFMMD is defined via an optimal witness function that is both smooth on the
graph and maximizes difference in expectation between the pair of distributions
on the graph. We find an analytical solution to this optimization problem as
well as an embedding of distributions that results from this method. We also
prove several properties of this method including scale invariance and
applicability to disconnected graphs. We showcase it on graph benchmark
datasets as well on single cell RNA-sequencing data analysis. In the latter, we
use the GFMMD-based gene embeddings to find meaningful gene clusters. We also
propose a novel type of score for gene selection called "gene localization
score" which helps select genes for cellular state space characterization.