The power of dynamic social networks to predict individuals’ mental health

Abstract

Precision medicine has received attention both in and outside the clinic. We focus on the latter, by exploiting the relationship between individuals' social interactions and their mental health to predict one's likelihood of being depressed or anxious from rich dynamic social network data. Existing studies differ from our work in at least one aspect: they do not model social interaction data as a network; they do so but analyze static network data; they examine "correlation" between social networks and health but without making any predictions; or they study other individual traits but not mental health. In a comprehensive evaluation, we show that our predictive model that uses dynamic social network data is superior to its static network as well as non-network equivalents when run on the same data. Supplementary material for this work is available at https://nd.edu/~cone/NetHealth/PSB_SM.pdf.