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.
Keywords: Social networks; big data analytics; data-driven modelling.