CitationSimpson, Olivia & McAuley, Julian (2016). Predicting risky behavior in social communities. arXiv.org. vol. 1606.08942v2
AbstractPredicting risk pro les of individuals in networks (e.g. susceptibility to a particular disease, or likelihood of smoking) is challenging for a variety of reasons. For one, `local' features (such as an individual's demographic information) may lack su cient information to make informative predictions; this is especially problematic when predicting `risk,' as the relevant features may be precisely those that an individual is disinclined to reveal in a survey. Secondly, even if such features are available, they still may miss crucial information, as `risk' may be a function not just of an individual's features but also those of their friends and social communities. Here, we predict individual's risk pro les as a function of both their local features and those of their friends. Instead of modeling in uence from the social network directly (which proved di cult as friendship links may be sparse and partially observed), we instead model in uence by discovering social communities in the network that may be related to risky behavior. The result is a model that predicts risk as a function of local features, while making up for their deficiencies and accounting for social in uence by uncovering community structure in the network. We test our model by predicting risky behavior among adolescents from the Add health data set, and hometowns among users in a Facebook ego net. Compared to prediction by features alone, our model demonstrates better predictive accuracy when measured as a whole, and in particular when measured as a function of network richness."
Reference TypeJournal Article