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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doaj-a38c23d72d3342c0879a803ee189c15f2020-11-25T02:19:47ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0182e5605710.1371/journal.pone.0056057Place-based attributes predict community membership in a mobile phone communication network.T Trevor CaughlinNick RuktanonchaiMiguel A AcevedoKenneth K LopianoOlivia ProsperNathan EagleAndrew J TatemSocial 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.http://europepmc.org/articles/PMC3579832?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
T Trevor Caughlin Nick Ruktanonchai Miguel A Acevedo Kenneth K Lopiano Olivia Prosper Nathan Eagle Andrew J Tatem |
spellingShingle |
T Trevor Caughlin Nick Ruktanonchai Miguel A Acevedo Kenneth K Lopiano Olivia Prosper Nathan Eagle Andrew J Tatem Place-based attributes predict community membership in a mobile phone communication network. PLoS ONE |
author_facet |
T Trevor Caughlin Nick Ruktanonchai Miguel A Acevedo Kenneth K Lopiano Olivia Prosper Nathan Eagle Andrew J Tatem |
author_sort |
T Trevor Caughlin |
title |
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_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_sort |
place-based attributes predict community membership in a mobile phone communication network. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2013-01-01 |
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. |
url |
http://europepmc.org/articles/PMC3579832?pdf=render |
work_keys_str_mv |
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