R-Mixup: Riemannian Mixup for Biological Networks
Xuan Kan Zimu Li Hejie Cui Yue Yu Ran Xu Shaojun Yu Zilong Zhang Ying Guo Carl Yang
Biological networks are commonly used in biomedical and healthcare domains to
effectively model the structure of complex biological systems with interactions
linking biological entities. However, due to their characteristics of high
dimensionality and low sample size, directly applying deep learning models on
biological networks usually faces severe overfitting. In this work, we propose
R-MIXUP, a Mixup-based data augmentation technique that suits the symmetric
positive definite (SPD) property of adjacency matrices from biological networks
with optimized training efficiency. The interpolation process in R-MIXUP
leverages the log-Euclidean distance metrics from the Riemannian manifold,
effectively addressing the swelling effect and arbitrarily incorrect label
issues of vanilla Mixup. We demonstrate the effectiveness of R-MIXUP with five
real-world biological network datasets on both regression and classification
tasks. Besides, we derive a commonly ignored necessary condition for
identifying the SPD matrices of biological networks and empirically study its
influence on the model performance. The code implementation can be found in
Appendix E.