AI 中文总结
针对基于登记处的社交网络建模难题，提出MARS图框架，复刻其构建方法，实现的简单模型可复现荷兰对应网络的属性，还揭示空间自由度与社会凝聚力的负相关关系。
AI 中文摘要
在可获取政府编制的正式微观数据的国家，基于登记处的社交网络已受到越来越多的关注。由于这类网络的生成过程复杂，现有随机图模型无法支持有效的结构分析，阻碍了对底层社会系统有意义洞见的发现。本文提出了多重关联基随机空间嵌入（Multiplex Affiliation-based Random Spatially-embedded，MARS）图框架，该框架复刻了基于登记处的社交网络的构建方法。我们推导了MARS集合在一般情况和特殊情况下的基本统计属性。为验证该框架的适用性，我们在MARS框架下实现了一个简单模型，结果显示该模型能复现荷兰人口规模的基于登记处的社交网络所呈现的相似属性。此外，我们分析了网络中空间连接强度对闭合性的影响，并将结果与现有实证发现对比，表明空间自由度的增加与社会凝聚力的降低相关。
英文摘要
Register-based social networks have become of increasing interest in countries where formal government-curated microdata is available. Due to the non-trivial generative process of register-based networks, existing random graph models fail to facilitate effective structural analysis, hindering the discovery of meaningful insights in the underlying social system. In this paper we introduce the Multiplex Affiliation-based Random Spatially-embedded (MARS) graph framework, which replicates the construction method of register-based social networks. We derive fundamental statistical properties of MARS ensembles in general and special cases. To demonstrate the applicability of the framework, we implement a simple model under the MARS framework and show that it recovers similar properties to those exhibited by the population-scale register-based social network of the Netherlands. Furthermore, we analyse the effect of spatial tie strength on closure in the network and compare our results with existing empirical findings, showing that increased spatial freedom is correlated with decreased social cohesion.
Comments22 pages, 9 figures
