Ego-centric Graphlets for Personality and Affective States Recognition

Do we tend to perceive ourselves more creative when surrounded by creative people? Or rather the opposite holds? Such information is very valuable to understand how to optimize work processes and boost people's productivity along with their happiness and satisfaction. Exploiting real-life data, collected over a period of six weeks in a research institution by means of wearable sensors, in this work we provide insights on human behavior dynamics in the workplace. We explore the use of graph lets, i.e. small induced sub graphs of a network, to encode the local structure of the interaction network of a subject, enriched with affective and personality states of his/her interaction partners. Our analysis shows that graph lets of increasing complexity, encoding non-trivial interaction patterns, are beneficial to affective and personality states recognition performance. We also find that different sensory channels, measuring proximity/co-location or face-to-face interactions, have different predictive power for distinct states.