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Place-Based Attributes Predict Community Membership in a Mobile Phone Communication Network

Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and th...

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Detalles Bibliográficos
Autores principales: Caughlin, T. Trevor, Ruktanonchai, Nick, Acevedo, Miguel A., Lopiano, Kenneth K., Prosper, Olivia, Eagle, Nathan, Tatem, Andrew J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3579832/
https://www.ncbi.nlm.nih.gov/pubmed/23451034
http://dx.doi.org/10.1371/journal.pone.0056057
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author Caughlin, T. Trevor
Ruktanonchai, Nick
Acevedo, Miguel A.
Lopiano, Kenneth K.
Prosper, Olivia
Eagle, Nathan
Tatem, Andrew J.
author_facet Caughlin, T. Trevor
Ruktanonchai, Nick
Acevedo, Miguel A.
Lopiano, Kenneth K.
Prosper, Olivia
Eagle, Nathan
Tatem, Andrew J.
author_sort Caughlin, T. Trevor
collection PubMed
description Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities. High modularity is also a property of large-scale social networks, where each node represents a population of individuals at a location, such as call flow between mobile phone towers. However, whether or not place-based attributes, including land cover and economic activity, can predict community membership for network nodes in large-scale networks remains unknown. We describe the pattern of modularity in a mobile phone communication network in the Dominican Republic, and use a linear discriminant analysis (LDA) to determine whether geographic context can explain community membership. Our results demonstrate that place-based attributes, including sugar cane production, urbanization, distance to the nearest airport, and wealth, correctly predicted community membership for over 70% of mobile phone towers. We observed a strongly positive correlation (r = 0.97) between the modularity score and the predictive ability of the LDA, suggesting that place-based attributes can accurately represent the processes driving modularity. In the absence of social network data, the methods we present can be used to predict community membership over large scales using solely place-based attributes.
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spelling pubmed-35798322013-02-28 Place-Based Attributes Predict Community Membership in a Mobile Phone Communication Network Caughlin, T. Trevor Ruktanonchai, Nick Acevedo, Miguel A. Lopiano, Kenneth K. Prosper, Olivia Eagle, Nathan Tatem, Andrew J. PLoS One Research Article Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities. High modularity is also a property of large-scale social networks, where each node represents a population of individuals at a location, such as call flow between mobile phone towers. However, whether or not place-based attributes, including land cover and economic activity, can predict community membership for network nodes in large-scale networks remains unknown. We describe the pattern of modularity in a mobile phone communication network in the Dominican Republic, and use a linear discriminant analysis (LDA) to determine whether geographic context can explain community membership. Our results demonstrate that place-based attributes, including sugar cane production, urbanization, distance to the nearest airport, and wealth, correctly predicted community membership for over 70% of mobile phone towers. We observed a strongly positive correlation (r = 0.97) between the modularity score and the predictive ability of the LDA, suggesting that place-based attributes can accurately represent the processes driving modularity. In the absence of social network data, the methods we present can be used to predict community membership over large scales using solely place-based attributes. Public Library of Science 2013-02-22 /pmc/articles/PMC3579832/ /pubmed/23451034 http://dx.doi.org/10.1371/journal.pone.0056057 Text en © 2013 Caughlin et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Caughlin, T. Trevor
Ruktanonchai, Nick
Acevedo, Miguel A.
Lopiano, Kenneth K.
Prosper, Olivia
Eagle, Nathan
Tatem, Andrew J.
Place-Based Attributes Predict Community Membership in a Mobile Phone Communication Network
title Place-Based Attributes Predict Community Membership in a Mobile Phone Communication Network
title_full Place-Based Attributes Predict Community Membership in a Mobile Phone Communication Network
title_fullStr Place-Based Attributes Predict Community Membership in a Mobile Phone Communication Network
title_full_unstemmed Place-Based Attributes Predict Community Membership in a Mobile Phone Communication Network
title_short Place-Based Attributes Predict Community Membership in a Mobile Phone Communication Network
title_sort place-based attributes predict community membership in a mobile phone communication network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3579832/
https://www.ncbi.nlm.nih.gov/pubmed/23451034
http://dx.doi.org/10.1371/journal.pone.0056057
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