An exciting avenue of neuroscientific research involves quantifying the time-varying prop-
erties of functional connectivity networks. As a result, many methods have been proposed to
estimate the dynamic properties of such networks. However, one of the challenges associated
with such methods involves the interpretation and visualization of high-dimensional, dynamic
networks. In this work, we employ graph embedding algorithms to provide low-dimensional
vector representations of networks, thus facilitating traditional objectives such as visualization,
interpretation and classification. We focus on linear graph embedding methods based on prin-
cipal component analysis and regularized linear discriminant analysis. The proposed graph
embedding methods are validated through a series of simulations and applied to fMRI data
from the Human Connectome Project.