Reconstructing the social network of HIV key populations from locally observed information

Abstract

Traditional surveys only provide local observations about the topological structure of isolated individuals. This study aims to develop a novel data-driven approach to reconstructing the social network of men who have sex with men (MSM) communities from locally observed information by surveys. A large social network consisting of 1075 users and their public relationships was obtained manually from BlueD.com. We followed the same survey-taking procedure to sample locally observed information and adapted an Exponential Random Graph Model (ERGM) to model the full structure of the BlueD social network (number of local nodes N = 1075, observed average degree k = 6.46). The parameters were learned and then used to reconstruct the MSM social networks by two real-world survey datasets in Hong Kong (N = 600, k = 5.61) and Guangzhou (N = 757, k = 5). Our method performed well on reconstructing the BlueD social network, with a high accuracy (90.3%). In conclusion, this study demonstrates the feasibility of using parameters learning methods to reconstruct the social networks of HIV key populations. The method has the potential to inform data-driven intervention programs that need global social network structures.