  Emotion prediction plays an essential role in mental health and emotion-aware
computing. The complex nature of emotion resulting from its dependency on a
person's physiological health, mental state, and his surroundings makes its
prediction a challenging task. In this work, we utilize mobile sensing data to
predict happiness and stress. In addition to a person's physiological features,
we also incorporate the environment's impact through weather and social
network. To this end, we leverage phone data to construct social networks and
develop a machine learning architecture that aggregates information from
multiple users of the graph network and integrates it with the temporal
dynamics of data to predict emotion for all the users. The construction of
social networks does not incur additional cost in terms of EMAs or data
collection from users and doesn't raise privacy concerns. We propose an
architecture that automates the integration of a user's social network affect
prediction, is capable of dealing with the dynamic distribution of real-life
social networks, making it scalable to large-scale networks. Our extensive
evaluation highlights the improvement provided by the integration of social
networks. We further investigate the impact of graph topology on model's
performance.
