Predicting User Roles in Social Networks using Transfer Learning with Feature Transformation

Communities of people are often modelled as social networks consisting of individual actors whose roles in the community correspond to the network patterns present around their corresponding nodes. Examples of such roles for individual actors in social networks are people bridging two communities, central people through which a large part of communication passes, and outliers. In social network analysis, recognising user roles is helpful to gain deeper understanding of the underlying communities. For large online social networks, the only scalable way to achieve this is through automatic labelling of nodes, i.e. using machine learning. If, in a community, persons are already annotated with roles (by whatever method), this can be exploited to train a classifier to detect person roles in case new people appear in the community.