Learn2Link: Linking the Social and Academic Profiles of Researchers


  • Asmelash Teka Hadgu Leibniz University Hannover
  • Jayanth Kumar Reddy Gundam Leibniz University of Hannover




People have presence across different information networks on the social web. The problem of user identity linking, is the task of establishing a connection between accounts of the same user across different networks. Solving this problem is useful for: personalized recommendations, cross platform data enrichment and verifying online information among others. In this paper, we propose a deep learning based approach that jointly models heterogeneous data: text content, network structure as well as profile names and images, in order to solve the user identity linking problem. We perform experiments on a real world problem of connecting the social profile (Twitter) and academic profile (DBLP) of researchers. Experimental results show that our joint model achieves a 97% F1 score outperforming state-of-the-art results that consider profile, content or network features only.




How to Cite

Hadgu, A. T., & Gundam, J. K. R. (2020). Learn2Link: Linking the Social and Academic Profiles of Researchers. Proceedings of the International AAAI Conference on Web and Social Media, 14(1), 240-249. https://doi.org/10.1609/icwsm.v14i1.7295