The Power of Dynamic Social Networks to Predict Individuals' Mental Health

Shikang Liu¹, David Hachen², Omar Lizardo³, Christian Poellabauer¹, Aaron Striegel¹, Tijana Milenković^1,*

¹Department of Computer Science and Engineering, University of Notre Dame
²Department of Sociology, University of Notre Dame
³Department of Sociology, University of California, Los Angeles
^*Corresponding author
Email: tmilenko@nd.edu

Pacific Symposium on Biocomputing 25:635-646(2020)

© 2020 World Scientific
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.

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.