DeepPick: A Deep Learning Approach to Unveil Outstanding Users Ranking with Public Attainable Features

Wanda Li, Zhiwei Xu, Yi Sun, Qingyuan Gong, Yang Chen, Aaron Yi Ding, Xin Wang, Pan Hui

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Outstanding users (OUs) denote the influential, 'core' or 'bridge' users in online social networks. How to accurately detect and rank them is an important problem for third-party online service providers and researchers. Conventional efforts, ranging from early graph-based algorithms to recent machine learning-based approaches, typically rely on an entire social network's information. However, for privacy-conscious users or newly-registered users, such information is not easily accessible. To address this issue, we present DeepPick, a novel framework that considers both the generalization and specialization in the detection task of OUs. For generalization, we introduce deep neural networks to capture dynamic features of the users. For specialization, we leverage the traditional descriptive features to make use of public information about users. Extensive experiments based on real-world datasets demonstrate that our approach achieves a high efficacy of detection performance against the state-of-the-art.
Original languageEnglish
Pages (from-to)291-306
Number of pages16
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number1
Publication statusPublished - 2023

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Bridges
  • Computer science
  • Deep Neural Networks
  • Feature extraction
  • Integrated circuit modeling
  • Neural networks
  • Online Social Networks
  • Outstanding User Detection
  • Social networking (online)
  • Task analysis


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