In social settings, individuals interact through webs of relationships. Eachindividual is a node in a complex network (or graph) of interdependencies andgenerates data, lots of data. We label the data by its source, or formallystated, we index the data by the nodes of the graph. The resulting signals(data indexed by the nodes) are far removed from time or image signals indexedby well ordered time samples or pixels. DSP, discrete signal processing,provides a comprehensive, elegant, and efficient methodology to describe,represent, transform, analyze, process, or synthesize these well ordered timeor image signals. This paper extends to signals on graphs DSP and its basictenets, including filters, convolution, z-transform, impulse response, spectralrepresentation, Fourier transform, frequency response, and illustrates DSP ongraphs by classifying blogs, linear predicting and compressing data fromirregularly located weather stations, or predicting behavior of customers of amobile service provider.
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