嵌入社会网络的异性二分网络的生成
Generating a Heterosexual Bipartite Network Embedded in Social Network
摘要：
我们描述了如何生成一个异性恋网络与规定的联合度分布，嵌入在一个规定的大规模的社会接触网络。性网络的结构在性传播感染（STIs）的传播中起着重要作用。生成一个模仿现实世界的网络集合，对于评估控制性传播感染的强有力的缓解战略至关重要。目前大多数生成性网络的算法只使用性活动数据，例如每月的伴侣数量，来生成性网络。现实世界的性网络也依赖于基于年龄、地点、社会和工作活动的有偏见的混合。我们描述了一种使用广泛的社交活动数据来生成可能的异性恋网络的方法。我们从一个大规模的模拟城市中的数千人开始，因为他们经历了他们的日常活动，包括工作，学校，购物和家庭活动。我们从这些活动中提取了一个社交网络，其中节点是人，边表示社交互动，例如在同一个位置工作。这个社交网络捕捉了不同年龄、居住在不同地点、经济状况和其他人口因素之间的相关性。我们使用社会联系网络来定义一个嵌入在扩展的社会网络中的双向异性恋网络。由此产生的性网络捕捉到了社会网络中固有的偏见混合，基于这种网络配对的模型可用于研究基于感染者社会接触的新型干预策略。我们说明了在异性恋网络中衣原体的传播模型的方法，代表了新奥尔良的年轻性活跃社区。
英文摘要：
We describe how to generate a heterosexual network with a prescribed joint-degree distribution that is embedded in a prescribed large-scale social contact network. The structure of a sexual network plays an important role in how sexually transmitted infections (STIs) spread. Generating an ensemble of networks that mimics the real-world is crucial to evaluating robust mitigation strategies for controling STIs. Most of the current algorithms to generate sexual networks only use sexual activity data, such as the number of partners per month, to generate the sexual network. Real-world sexual networks also depend on biased mixing based on age, location, and social and work activities. We describe an approach to use a broad range of social activity data to generate possible heterosexual networks. We start with a large-scale simulation of thousands of people in a city as they go through their daily activities, including work, school, shopping, and activities at home. We extract a social network from these activities where the nodes are the people and the edges indicate a social interaction, such as working in the same location. This social network captures the correlations between people of different ages, living in different locations, their economic status, and other demographic factors. We use the social contact network to define a bipartite heterosexual network that is embedded within an extended social network. The resulting sexual network captures the biased mixing inherent in the social network, and models based on this pairing of networks can be used to investigate novel intervention strategies based on the social contacts of infected people. We illustrate the approach in a model for the spread of Chlamydia in the heterosexual network representing the young sexually active community in New Orleans.
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