A scalable signal processing architecture for massive graph analysis

@article{Miller2012ASS,
  title={A scalable signal processing architecture for massive graph analysis},
  author={Benjamin A. Miller and Nicholas Arcolano and Michelle S. Beard and Jeremy Kepner and Matthew C. Schmidt and Nadya T. Bliss and Patrick J. Wolfe},
  journal={2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2012},
  pages={5329-5332}
}
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… CONTINUE READING
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