@inproceedings{ea2fbc4ddb0d4cc297916d1f340d9d11,
title = "A scalable signal processing architecture for massive graph analysis",
abstract = "In many applications, it is convenient to represent data as a graph, and often these datasets will be quite large. This paper presents an architecture for analyzing massive graphs, with a focus on signal processing applications such as modeling, filtering, and signal detection. We describe the architecture, which covers the entire processing chain, from data storage to graph construction to graph analysis and subgraph detection. The data are stored in a new format that allows easy extraction of graphs representing any relationship existing in the data. The principal analysis algorithm is the partial eigendecomposition of the modularity matrix, whose running time is discussed. A large document dataset is analyzed, and we present subgraphs that stand out in the principal eigenspace of the time-varying graphs, including behavior we regard as clutter as well as small, tightly-connected clusters that emerge over time.",
keywords = "Graph theory, emergent behavior, large data analysis, processing architectures, residuals analysis",
author = "Miller, {Benjamin A.} and Nicholas Arcolano and Beard, {Michelle S.} and Jeremy Kepner and Schmidt, {Matthew C.} and Bliss, {Nadya T.} and Wolfe, {Patrick J.}",
year = "2012",
doi = "10.1109/ICASSP.2012.6289124",
language = "English (US)",
isbn = "9781467300469",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "5329--5332",
booktitle = "2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings",
note = "2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 ; Conference date: 25-03-2012 Through 30-03-2012",
}