从公开数据推断城市社交网络
Inferring urban social networks from publicly available data
摘要：
社交网络的出现和合成但现实的社会图的合适的生成模型的定义是文献中广泛研究的问题。由于不与任何真实数据相关联，随机图模型无法捕捉真实网络的所有微妙之处，并且不适用于许多实际情况-包括计算流行病学等研究领域，这些领域最近被列为议程的重点。与此同时，所谓的接触网络描述的是相互作用，而不是关系，并且强烈依赖于应用程序以及用于推断它们的样本数据的大小和质量。为了填补这两种方法之间的差距，我们提出了一个数据驱动的城市社交网络模型，实现和发布的开源软件。给定一个感兴趣的领域，并且仅基于广泛可用的汇总人口统计和社会混合数据，我们构建了一个年龄分层和地理参考的合成人口，其个体通过两种类型的“强关系”连接：家庭内（例如，亲属关系或友谊。虽然家庭联系完全是数据驱动的，但我们提出了一个友谊的参数概率模型，该模型基于距离和年龄差异起作用的假设，并且并非所有个体都具有同样的社交能力。人口和地理因素所获得的网络结构，在不同的配置下，通过广泛的模拟集中在三个不同规模的意大利城市进行了深入研究。
英文摘要：
The emergence of social networks and the definition of suitable generative models for synthetic yet realistic social graphs are widely studied problems in the literature. By not being tied to any real data, random graph models cannot capture all the subtleties of real networks and are inadequate for many practical contexts -- including areas of research, such as computational epidemiology, which are recently high on the agenda. At the same time, the so-called contact networks describe interactions, rather than relationships, and are strongly dependent on the application and on the size and quality of the sample data used to infer them. To fill the gap between these two approaches, we present a data-driven model for urban social networks, implemented and released as open source software. Given a territory of interest, and only based on widely available aggregated demographic and social-mixing data, we construct an age-stratified and geo-referenced synthetic population whose individuals are connected by "strong ties" of two types: intra-household (e.g., kinship) or friendship. While household links are entirely data-driven, we propose a parametric probabilistic model for friendship, based on the assumption that distances and age differences play a role, and that not all individuals are equally sociable. The demographic and geographic factors governing the structure of the obtained network, under different configurations, are thoroughly studied through extensive simulations focused on three Italian cities of different size.
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