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dc.contributor.authorEagle, Nathan N.
dc.contributor.authorCaughlin, T. Trevor
dc.contributor.authorRuktanonchai, Nick
dc.contributor.authorAcevedo, Miguel A.
dc.contributor.authorLopiano, Kenneth K.
dc.contributor.authorProsper, Olivia
dc.contributor.authorTatem, Andrew J.
dc.date.accessioned2013-04-16T17:29:21Z
dc.date.available2013-04-16T17:29:21Z
dc.date.issued2013-02
dc.date.submitted2012-07
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/1721.1/78553
dc.description.abstractSocial 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.en_US
dc.language.isoen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofhttp://dx.doi.org/10.1371/journal.pone.0056057en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttp://creativecommons.org/licenses/by/2.5/en_US
dc.sourcePLoSen_US
dc.titlePlace-Based Attributes Predict Community Membership in a Mobile Phone Communication Networken_US
dc.typeArticleen_US
dc.identifier.citationCaughlin, T. Trevor et al. “Place-Based Attributes Predict Community Membership in a Mobile Phone Communication Network.” Ed. Angel Sánchez. PLoS ONE 8.2 (2013): e56057.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratoryen_US
dc.contributor.mitauthorEagle, Nathan N.
dc.relation.journalPLoS ONEen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsCaughlin, T. Trevor; Ruktanonchai, Nick; Acevedo, Miguel A.; Lopiano, Kenneth K.; Prosper, Olivia; Eagle, Nathan; Tatem, Andrew J.en
mit.licensePUBLISHER_CCen_US
mit.metadata.statusComplete


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