在资源有限的环境中构建大规模社会中心网络的半监督记录链接：乌干达和肯尼亚农村地区SEARCH研究的应用
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
摘要：
本文提出了一种新的半监督算法的方法来创建大规模的社会中心网络在东非农村。我们描述了32个大型社会中心的社交网络在农村撒哈拉以南非洲的建设。通过将半监督记录链接算法应用于包括在乌干达和肯尼亚的社区群随机艾滋病毒预防试验中的32个社区的人口普查枚举居民的数据来构建网络。使用五个问题名称生成器在情感支持、食物分享、空闲时间、健康问题和金钱问题等领域征求联系人。完整构建的网络包括170; 028个节点和362; 965条边，这些边跨社区聚合（每个社区的节点从4449到6829，边从2349到31,779）。我们的算法平均匹配30%的命名接触在肯尼亚社区和50%的命名接触在乌干达社区的居民在人口普查枚举命名。八个不同的协变量的分类混合措施表明，在网络中的居民有一个非常强烈的倾向，与其他人谁是类似于他们的年龄，性别，尤其是村庄。该研究中的网络将为更好地了解东非农村地区的卫生成果提供一个平台。我们提出的网络构建算法可能有助于未来的社会网络研究在资源有限的设置。
英文摘要：
This paper presents a novel semi-supervised algorithmic approach to creating large scale sociocentric networks in rural East Africa. We describe the construction of 32 large-scale sociocentric social networks in rural Sub-Saharan Africa. Networks were constructed by applying a semi-supervised record-linkage algorithm to data from census-enumerated residents of the 32 communities included in the SEARCH study (NCT01864603), a community-cluster randomized HIV prevention trial in Uganda and Kenya. Contacts were solicited using a five question name generator in the domains of emotional support, food sharing, free time, health issues and money issues. The fully constructed networks include 170; 028 nodes and 362; 965 edges aggregated across communities (ranging from 4449 to 6829 nodes and from 2349 to 31,779 edges per community). Our algorithm matched on average 30% of named contacts in Kenyan communities and 50% of named contacts in Ugandan communities to residents named in census enumeration. Assortative mixing measures for eight different covariates reveal that residents in the network have a very strong tendency to associate with others who are similar to them in age, sex, and especially village. The networks in the SEARCH Study will provide a platform for improved understanding of health outcomes in rural East Africa. The network construction algorithm we present may facilitate future social network research in resource-limited settings.
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