Temporal network tracking based on tensor factor analysis of graph signal spectrum

A wide variety of networks, ranging from biological to social, evolve, adapt and change over time. Recent methods employed in the assessment of temporal networks include tracking topological graph metrics, evolutionary clustering, tensor based anomaly methods and, more recently, graph to signal transformations. In this paper, we propose to assess the temporal evolution of networks by first transforming networks into signals through Classical Multidimensional Scaling based on the resistance distance and then constructing a tensor based on the spectra of each signal across time. The proposed method is first evaluated on simulated temporal networks with varying structural properties. Next, the method is applied to temporal functional connectivity networks constructed from multichannel electroencephalogram (EEG) data collected during a study of cognitive control. This analysis shows that the proposed method is more sensitive to changes in the network structure and more robust to variations in edge weights.