The effect of transmission variance on observer placement for source-localization

Abstract Detecting where an epidemic started, i.e., which node in a network was the source, is of crucial importance in many contexts. However, finding the source of an epidemic can be challenging, especially because the information available is often sparse and noisy. We consider a setting in which...

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Main Authors: Brunella Spinelli, L. Elisa Celis, Patrick Thiran
Format: Article
Language:English
Published: SpringerOpen 2017-07-01
Series:Applied Network Science
Subjects:
Online Access:http://link.springer.com/article/10.1007/s41109-017-0040-5
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spelling doaj-5bfb67ff2aeb4794a85495152feec0a22020-11-24T22:22:23ZengSpringerOpenApplied Network Science2364-82282017-07-012112610.1007/s41109-017-0040-5The effect of transmission variance on observer placement for source-localizationBrunella Spinelli0L. Elisa Celis1Patrick Thiran2École Polytechnique Fédérale de Lausanne (EPFL)École Polytechnique Fédérale de Lausanne (EPFL)École Polytechnique Fédérale de Lausanne (EPFL)Abstract Detecting where an epidemic started, i.e., which node in a network was the source, is of crucial importance in many contexts. However, finding the source of an epidemic can be challenging, especially because the information available is often sparse and noisy. We consider a setting in which we want to localize the source based exclusively on the information provided by a small number of observers – i.e., nodes that can reveal if and when they are infected – and we study where such observers should be placed. We show that the optimal observer placement depends not only on the topology of the network, but also on the variance of the node-to-node transmission delays. We consider both low-variance and high-variance regimes for the transmission delays and propose algorithms for observer placement in both cases. In the low-variance regime, it suffices to only consider the network-topology and to choose observers that, based on their distances to all other nodes in the network, can distinguish among possible sources. However, the high-variance regime requires a new approach in order to guarantee that the observed infection times are sufficiently informative about the location of the source and do not get masked by the noise in the transmission delays; this is accomplished by additionally ensuring that the observers are not placed too far apart. We validate our approaches with simulations on three real-world networks. Compared to state-of-the-art strategies for observer placement, our methods have a better performance in terms of source-localization accuracy for both the low- and the high-variance regimes.http://link.springer.com/article/10.1007/s41109-017-0040-5Source localizationEpidemicsSensor placement
collection DOAJ
language English
format Article
sources DOAJ
author Brunella Spinelli
L. Elisa Celis
Patrick Thiran
spellingShingle Brunella Spinelli
L. Elisa Celis
Patrick Thiran
The effect of transmission variance on observer placement for source-localization
Applied Network Science
Source localization
Epidemics
Sensor placement
author_facet Brunella Spinelli
L. Elisa Celis
Patrick Thiran
author_sort Brunella Spinelli
title The effect of transmission variance on observer placement for source-localization
title_short The effect of transmission variance on observer placement for source-localization
title_full The effect of transmission variance on observer placement for source-localization
title_fullStr The effect of transmission variance on observer placement for source-localization
title_full_unstemmed The effect of transmission variance on observer placement for source-localization
title_sort effect of transmission variance on observer placement for source-localization
publisher SpringerOpen
series Applied Network Science
issn 2364-8228
publishDate 2017-07-01
description Abstract Detecting where an epidemic started, i.e., which node in a network was the source, is of crucial importance in many contexts. However, finding the source of an epidemic can be challenging, especially because the information available is often sparse and noisy. We consider a setting in which we want to localize the source based exclusively on the information provided by a small number of observers – i.e., nodes that can reveal if and when they are infected – and we study where such observers should be placed. We show that the optimal observer placement depends not only on the topology of the network, but also on the variance of the node-to-node transmission delays. We consider both low-variance and high-variance regimes for the transmission delays and propose algorithms for observer placement in both cases. In the low-variance regime, it suffices to only consider the network-topology and to choose observers that, based on their distances to all other nodes in the network, can distinguish among possible sources. However, the high-variance regime requires a new approach in order to guarantee that the observed infection times are sufficiently informative about the location of the source and do not get masked by the noise in the transmission delays; this is accomplished by additionally ensuring that the observers are not placed too far apart. We validate our approaches with simulations on three real-world networks. Compared to state-of-the-art strategies for observer placement, our methods have a better performance in terms of source-localization accuracy for both the low- and the high-variance regimes.
topic Source localization
Epidemics
Sensor placement
url http://link.springer.com/article/10.1007/s41109-017-0040-5
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