Signal Processing over Multilayer Graphs: Theoretical Foundations and Practical Applications

Signal processing over single-layer graphs has become a mainstream tool owing to its power in revealing obscure underlying structures within data signals. However, many real-life datasets and systems, including those Internet of Things (IoT), are characterized by more complex interactions among distinct entities, which may represent multi-level that harder be captured with graph, can better multilayers graph connections. Such multilayer or naturally modeled high-dimensional (MLG). To generalize traditional signal (GSP) for analyzing features their interactions, this work proposes tensor-based framework (M-GSP). Specifically, we introduce core concepts M-GSP study properties MLG spectral space, followed fundamentals MLG-based filter design. illustrate novel aspects M-GSP, further explore link GSP. We provide example applications demonstrate the efficacy benefits applying practical scenarios.