Observing and Modeling User Behavior on Socio-Spatial Interaction Networks: Conformance, Exceptions, and Anomalies

For modeling user behavior in Artificial Intelligence (AI) systems, we can make use of diverse heterogeneous data sources. This paper investigates socio-spatial interaction networks for modeling user interactions from three perspectives: We analyze preferences and perceptions of human social interactions in relation to the interactions observed using wearable sensors. For that, we investigate the correspondence of according networks, in order to identify conformance, exceptions, and anomalies. The analysis is performed on a real-world dataset capturing networks of proximity interactions coupled with self-report questionnaires about preferences and perception of those interactions.