Title
A Controlled Sensing Approach to Graph Classification
Abbreviated Journal Title
Publ. Astron. Soc. Pac.
Keywords
Complex networks; controlled sensing; estimation theory; graph; classification; social networks; SOCIAL NETWORK; Engineering, Electrical & Electronic
Abstract
The problem of classifying graphs with respect to connectivity via partial observations of nodes is posed as a composite hypothesis testing problem with controlled sensing. An observation at a node is a subset of edges incident to the node on the complete graph drawn according to a probability model, which is a function of a fixed underlying graph. Connectivity is measured through average node degree and is classified with respect to a threshold. A simple approximation of the controlled sensing test is derived and simulated on Erdos-Renyi graphs to characterize the error probabilities as a function of the expected stopping times. The test is also experimentally validated on a real-world example of the social structure of Long-Tailed Manakins. It is shown that the proposed test achieves favorable tradeoffs between the classification error and the number of measurements. Furthermore, the test outperforms existing approaches, especially at low target error rates. In addition, the proposed test achieves the optimal error exponent.
Subjects
J. G. Ligo; G. K. Atia;V. V. Veeravalli
Volume
62
Issue/Number
24
Publication Date
1-1-2014
Document Type
Article
Language
English
First Page
6468
Last Page
6480
WOS Identifier
ISSN
1053-587X
Recommended Citation
"A Controlled Sensing Approach to Graph Classification" (2014). Faculty Bibliography 2010s. 5697.
https://stars.library.ucf.edu/facultybib2010/5697
Comments
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