  In social settings, individuals interact through webs of relationships. Each
individual is a node in a complex network (or graph) of interdependencies and
generates data, lots of data. We label the data by its source, or formally
stated, 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 indexed
by 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 time
or image signals. This paper extends to signals on graphs DSP and its basic
tenets, including filters, convolution, z-transform, impulse response, spectral
representation, Fourier transform, frequency response, and illustrates DSP on
graphs by classifying blogs, linear predicting and compressing data from
irregularly located weather stations, or predicting behavior of customers of a
mobile service provider.
