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Graph signal processing extends the notion of frequency from signals in the time domain to signals defined on graphs. Graph signals arise in many applications including brain signals defined on functional connectivity networks. Most of the current work on graph signal processing focuses on static graphs. However, functional connectivity networks are dynamic and the signals on these networks change...
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...
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