Social Role Identification via Dual Uncertainty Minimization Regularization

Yu Cheng, Ankit Agrawal, Alok Nidhi Choudhary, Huan Liu, Tao Zhang

Research output: Contribution to journalConference articlepeer-review

7 Scopus citations


In this paper, we study a challenging problem of inferring individuals' role and statuses in a professional social network, which is of central importance in workforce optimization and human capital management. Realizing the natural setting of social nodes associated with dual view information, i.e., The local node characteristics and the global network influence, we present a novel model that explores graph regularization techniques and integrates such information to achieve improved prediction performance. In particular, our prediction model is built upon the graph transductive learning framework that encodes an uncertainty regularization term in the conventional empirical risk minimization principle. Through taking advantage of the information from both the local profile and the global network characteristics, the final inference of the role or statues achieves minimum an empirical loss on the labeled set, as well as a minimum uncertainty on the unlabeled social nodes. We perform extensive empirical study using real-world data and compare with representative peer approaches. The experimental results on three real social network data sets show that the proposed model greatly outperforms a number of baseline models and is able to effectively infer in a wide range of scenarios.

Original languageEnglish (US)
Article number7023398
Pages (from-to)767-772
Number of pages6
JournalProceedings - IEEE International Conference on Data Mining, ICDM
Issue numberJanuary
StatePublished - Jan 26 2015
Event14th IEEE International Conference on Data Mining, ICDM 2014 - Shenzhen, China
Duration: Dec 14 2014Dec 17 2014


  • Dual Uncertainty Minimization
  • Graph Regularization
  • Social Role Identification

ASJC Scopus subject areas

  • General Engineering


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