Title

A Controlled Sensing Approach to Graph Classification

Authors

Authors

IEEE Trans. Signal Process.

Comments

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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

WOS:000345516000010

ISSN

1053-587X

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