A scalable signal processing architecture for massive graph analysis

Benjamin A. Miller, Nicholas Arcolano, Michelle S. Beard, Jeremy Kepner, Matthew C. Schmidt, Nadya T. Bliss, Patrick J. Wolfe

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages5329-5332
Number of pages4
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: 25 Mar 201230 Mar 2012

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
Country/TerritoryJapan
CityKyoto
Period25/03/1230/03/12

Keywords

  • emergent behavior
  • Graph theory
  • large data analysis
  • processing architectures
  • residuals analysis

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'A scalable signal processing architecture for massive graph analysis'. Together they form a unique fingerprint.

Cite this