Signal Processing has traditionally dealt with time series, images, video where data is indexed by time ticks and pixels. The structure of the indexing set is taken for granted. In the last few years, new opportunities for signal and data processing have arisen, except data is now indexed by social agents, genes, customers of service providers, or by some other arbitrary enumeration suggested by the application. We develop Signal Processing on Graphs by revisiting the fundamentals of Signal Processing, developing for data (signals) arising from these various domains the essential concepts and methods of traditional Signal Processing. We illustrate the approach with data drawn from a number of different applications including social networks and customer data. Ours is an attempt to identify structure in unstructured data and theory and modeling in the “data deluge.”
 The End of Theory: The Data Deluge Makes The Scientific Method Obsolete, Chris Anderson, Wired Magazine, June 23, 2008.
José M. F. Moura is the Philip L. and Marsha Dowd University Professor at CMU, with interests in signal processing and data science. He coinvented (with ALEK Kavcic) a patented detector found in at least 60% of the disk drives of all computers sold worldwide in the last 12 years (over 3 billion and counting). He is (2016) IEEE VP for Technical Activities, IEEE Board Director, and was President of the IEEE Signal Processing Society (SPS), and Editor in Chief for the Transactions on SP. Moura received the IEEE SPS Technical Achievement Award and Society Award. He is Fellow of the IEEE and of AAAS, corresponding member of the Academy of Sciences of Portugal, Fellow of the US National Academy of Innovators, and member of the US National Academy of Engineering.