Predicting User Roles in Social Networks using Transfer Learning with Feature Transformation
arXiv:1611.02941v126 citations
Originality Synthesis-oriented
AI Analysis
This work addresses role classification in social networks for researchers and practitioners, but it appears incremental as it builds on existing transfer learning methods.
The paper tackled the problem of recognizing social roles in unlabeled social networks by proposing a transfer learning approach with feature transformations, achieving results on real-world datasets.
How can we recognise social roles of people, given a completely unlabelled social network? 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. Experiments are carried out on real-world datasets. (See manuscript for the full abstract.)