Valid two‐sample graph testing via optimal transport Procrustes and multiscale graph correlation with applications in connectomics
Abstract
Testing whether two graphs come from the same distribution is of interest in many real‐world scenarios, including brain network analysis. Under the random dot product graph model, the nonparametric hypothesis testing framework consists of embedding the graphs using the adjacency spectral embedding (ASE), followed by aligning the embeddings using the median flip heuristic and finally applying the nonparametric maximum mean discrepancy (MMD) test to obtain a p value. Using synthetic data generated from Drosophila brain networks, we show that the median flip heuristic results in an invalid test and demonstrate that optimal transport Procrustes (OTP) for alignment resolves the invalidity. We further demonstrate that substituting the MMD test with the multiscale graph correlation (MGC) test leads to a more powerful test both in synthetic and in simulated data. Lastly, we apply this powerful test to the right and left hemispheres of the larval Drosophila mushroom body brain networks and conclude that there is not sufficient evidence to reject the null hypothesis that the two hemispheres are equally distributed.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 02, 2022
- Source ID
- 10.1002/sta4.429
Entities
People
- Anton Alyakin
- Bijan Varjavand
- Carey E. Priebe
- Jaewon Chung
- Jesús Arroyo‐relión
- Joshua Agterberg
- Joshua T Vogelstein
- Minh Tang
Organizations
- Air Force Research Laboratory
- Defense Advanced Research Projects Agency
- Johns Hopkins University
- Microsoft Research
- National Science Foundation
- North Carolina State University
- Statistics New Zealand