Learn2Link: Linking the Social and Academic Profiles of Researchers

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

Abstract

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.

Published
2020-05-26
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. Retrieved from https://aaai.org/ojs/index.php/ICWSM/article/view/7295