13: Distance-based Repeated Measures MANOVA for Longitudinal Network on Spherical Surface

Music City Center 

Longitudinal network data reflects the dynamic evolution of network structures and attributes over time, offering a unique opportunity to explore temporal dynamics, uncovering trends, and identifying the mechanisms driving network evolution. These insights are particularly valuable in areas such as social networks, biological systems, communication networks, and neuroscience/neurology. In this study, we introduce a novel non-parametric hypothesis-testing method specifically tailored for longitudinal network data on spherical surface. The proposed method begins with the construction of a network distance matrix on manifold, and accounts for the impact of serial correlation across multiple time points, ensuring temporal dependencies are appropriately addressed. Experiments on both synthetic and real-world data demonstrate that the proposed method effectively controls type I errors while maintaining robust statistical power to detect group or time effects and their interactions in network data.

Longitudinal Network Analysis

Distance-based Repeated Measures MANOVA

Manifold Learning 

Section on Nonparametric Statistics