Graph Signal Processing (GSP) extends classical signal processing techniques to data residing on irregular structures represented as graphs, enabling the analysis of signals with complex interdependencies. It leverages graph theory and spectral methods to generalize concepts like filtering, sampling, and Fourier transforms to graph-based domains, providing powerful tools for applications in network science, machine learning, and data analysis.