Data Science with Graphs: A Signal Processing Perspective
A massive amount of data is being generated at an unprecedented level from a diversity of sources, including social media, internet services, biological studies, physical infrastructure monitoring and many others. The necessity of analyzing such complex data has led to the birth of an emerging framework, graph signal processing. This framework offers an unified and mathematically rigorous paradigm for the analysis of high-dimensional data with complex and irregular structure. It extends fundamental signal processing concepts such as signals, Fourier transform, frequency response and filtering, from signals residing on regular lattices, which have been studied by the classical signal processing theory, to data residing on general graphs, which are called graph signals. In this thesis, we consider five fundamental tasks on graphs from the perspective of graph signal processing: representation, sampling, recovery, detection and localization. Representation, aiming to concisely model shapes of graph signals, is at the heart of the proposed techniques. Sampling followed by recovery, aiming to reconstruct an original graph signal from a few selected samples, is applicable in semi-supervised learning and user profiling in online social networks. Detection followed by localization, aiming to identify and localize targeted patterns in noisy graph signals, is related to many real-world applications, such as localizing virus attacks in cyber-physical systems, localizing stimuli in brain connectivity networks, and mining traffic events in city street networks, to name just a few. We illustrate the power of the proposed tools on two real-world problems: fast resampling of 3D point clouds and mining of urban traffic data.