  Predicting risk profiles 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 sufficient 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 profiles as a function of both their local features and those
of their friends. Instead of modeling influence from the social network
directly (which proved difficult as friendship links may be sparse and
partially observed), we instead model influence 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 influence 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."
