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How can we recognise social roles of people, given a completely unlabelled social network? We may train a role classification algorithm on another dataset, but then that dataset may have largely different values of its features, for instance, the degrees in the other network may be distributed in a completely different way than in the first network. Thus, a way to transfer the features of different networks to each other or to a common feature space is needed. This type of setting is called transfer learning. In this paper, we present a transfer learning approach to network role classification based on feature transformations from each network's local feature distribution to a global feature space. We implement our approach and show experiments on real-world networks of discussions on Wikipedia as well as online forums. We also show a concrete application of our approach to an enterprise use case, where we predict the user roles in ARIS Community, the online platform for customers of Software AG, the second-largest German software vendor. Evaluation results show that our approach is suitable for transferring knowledge of user roles across networks.

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