2023/05/31 by Lital Dabush, Tirza Routtenberg, Dabush, Lital +1 · 3 citations
Computer Science · Engineering · Physics and Astronomy · #Advanced Graph Neural Networks #Complex Network Analysis Techniques #FOS: Electrical engineering #Signal Processing (eess.SP) #Smart Grid Security and Resilience #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2305.19618
openalex publication_date 2023/05/31 · openalex created_date 2023/06/02 · openalex updated_date 2026/07/28
Graph signal processing (GSP) deals with the representation, analysis, and processing of structured data, i.e. graph signals that are defined on the vertex set of a generic graph. A crucial prerequisite for applying various GSP and graph neural network (GNN) approaches is that the examined signals are smooth graph signals with respect to the underlying graph, or, equivalently, have low graph total variation (TV). In this paper, we develop GSP-based approaches to verify the validity of the smoothness assumption of given signals (data) and an associated graph. The proposed approaches are based on the representation of network data as the output of a graph filter with a given graph topology. In particular, we develop two smoothness detectors for the graph-filter-output model: 1) the likelihood ratio test (LRT) for known model parameters; and 2) a semi-parametric detector that estimates the graph filter and then validates its smoothness. The properties of the proposed GSP-based detectors are investigated, and some special cases are discussed. The performance of the GSP-based detectors is evaluated using synthetic data, data from the IEEE 14-bus power system, and measurements from a network of light intensity sensors, under different setups. The results demonstrate the effectiveness of the proposed approach and its robustness to different generating models, noise levels, and number of samples.