Big Data Analysis with Signal Processing on Graphs: Representation and processing of massive data sets with irregular structure

By Aliaksei Sandryhaila, José M. F. Moura. IEEE Signal Processing Magazine, 2014

748 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/65d61afd9c35b0a75d9de77c2a4a2428af0f7f7b

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In Artificial Intelligence

In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

Efficient Sampling Set Selection for Bandlimited Graph Signals Using Graph Spectral Proxies (2015, background): “Graph signal processing aims to extend the well-developed tools for analysis of conventional signals to signals on graphs while exploiting the underlying connectivity information [1], [2].”
Compressive Spectral Clustering (2016, methodology): “In this work, inspired by recent advances in the emerging ﬁeld of graph signal processing (Sandryhaila & Moura, 2014; Shuman et al., 2013), we circumvent SC’s last two bottlenecks and detail a fast approximate spectral clustering method for large datasets, as well as the supporting theory.”
Big Data Analytics in Healthcare (2015, methodology): “By illustrating the data with a graph model, a framework for analyzing large-scale data has been presented [59].”
Signals on Graphs: Uncertainty Principle and Sampling (2015, background): “I N many applications, from sensor to social networks, transportation systems, gene regulatory networks or big data, the signals of interest are deﬁned over the vertices of a graph [1], [2].”
A survey of machine learning for big data processing (2016, background): “Large-scale data sets are collected and studied in numerous domains, from engineering sciences to social networks, commerce, biomolecular research, and security [1].”
Topology adaptive graph convolutional networks (2017, background): “Matrix F deﬁnes the graph Fourier transform (Sandryhaila & Moura, 2013; 2014), and Fx ( (cid:96) ) c is the input feature spectrum map, which is a linear mapping from the input feature on the vertex domain to the spectrum domain.”
End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications (2017, background): “Efficient data processing and storage systems and algorithms formassive spectrum data analytics [35] are needed to extract valuable information from such data and incorporate it into the spectrum decision/policy process in real-time.”
On the degrees of freedom of signals on graphs (2015, background): “Of particular interest is the application of this relatively new discipline to the analysis of big data, as proposed in [3].”
Graph Signal Processing: Overview, Challenges, and Applications (2017, background): “lines; some parts of the exposition follow closely [2], [45].”