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Proceedings Paper

Finding structures in large-scale graphs
Author(s): Sang Peter Chin; Elizabeth Reilly; Linyuan Lu
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Paper Abstract

One of the most vexing challenges of working with graphical structures is that most algorithms scale poorly as the graph becomes very large. The computation is extremely expensive even for polynomial algorithms, thus making it desirable to devise fast approximation algorithms. We herein propose a framework using advanced tools 1-6 from random graph theory and spectral graph theory to address the quantitative analysis of the structure and dynamics of large-scale networks. This framework enables one to carry out analytic computations of observable network structures and capture the most relevant and refined quantities of realworld networks.

Paper Details

Date Published: 6 July 2012
PDF: 8 pages
Proc. SPIE 8408, Cyber Sensing 2012, 840805 (6 July 2012); doi: 10.1117/12.978069
Show Author Affiliations
Sang Peter Chin, Johns Hopkins Univ. Applied Physics Lab. (United States)
Elizabeth Reilly, John Hopkins Univ. Applied Physics Lab. (United States)
Linyuan Lu, Univ. of South Carolina (United States)

Published in SPIE Proceedings Vol. 8408:
Cyber Sensing 2012
Igor V. Ternovskiy; Peter Chin, Editor(s)

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