We describe an approach to generate a heterosexual network with a prescribed joint-degree distribution embedded in a prescribed large-scale social contact network. The structure of a sexual network plays an important role in how all sexually transmitted infections (STIs) spread. Generating an ensemble of networks that mimics the real-world is crucial to evaluating robust mitigation strategies for controlling 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 among 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.

我们描述了一种生成异性恋网络的方法，该网络具有嵌入规定的大规模社交联系网络中的规定的联合度分布。性网络的结构在所有性传播感染 (STI) 的传播方式中发挥着重要作用。生成模仿现实世界的网络集合对于评估控制性传播感染的稳健缓解策略至关重要。目前大多数生成性网络的算法仅使用性活动数据（例如每月的伴侣数量）来生成性网络。现实世界的性网络还依赖于基于年龄、地点、社交和工作活动的偏见混合。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。我们首先对一个城市中数千人的日常活动进行大规模模拟，包括工作、上学、购物和家庭活动。我们从这些活动中提取一个社交网络，其中节点是人，边缘表示社交互动，例如在同一位置工作。这个社交网络捕捉了不同年龄、生活在不同地点的人、他们的经济状况和其他人口统计因素之间的相关性。我们使用社交联系网络来定义嵌入在扩展社交网络中的双向异性恋网络。由此产生的性网络捕获了社交网络中固有的偏见混合，并且基于这种网络配对的模型可用于研究基于感染者之间的社会接触的新干预策略。我们在代表新奥尔良年轻性活跃社区的异性恋网络中衣原体传播模型中说明了该方法。