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This paper presents a novel semi-supervised algorithmic approach to creatinglarge scale sociocentric networks in rural East Africa. We describe theconstruction of 32 large-scale sociocentric social networks in ruralSub-Saharan Africa. Networks were constructed by applying a semi-supervisedrecord-linkage algorithm to data from census-enumerated residents of the 32communities included in the SEARCH study (NCT01864603), a community-clusterrandomized HIV prevention trial in Uganda and Kenya. Contacts were solicitedusing a five question name generator in the domains of emotional support, foodsharing, free time, health issues and money issues. The fully constructednetworks include 170; 028 nodes and 362; 965 edges aggregated acrosscommunities (ranging from 4449 to 6829 nodes and from 2349 to 31,779 edges percommunity). Our algorithm matched on average 30% of named contacts in Kenyancommunities and 50% of named contacts in Ugandan communities to residents namedin census enumeration. Assortative mixing measures for eight differentcovariates reveal that residents in the network have a very strong tendency toassociate with others who are similar to them in age, sex, and especiallyvillage. The networks in the SEARCH Study will provide a platform for improvedunderstanding of health outcomes in rural East Africa. The network constructionalgorithm we present may facilitate future social network research inresource-limited settings.

Semi-Supervised Record Linkage for Construction of Large-Scale  Sociocentric Networks in Resource-limited Settings: An application to the  SEARCH Study in Rural Uganda and Kenya