Title
DeepPick: A Deep Learning Approach to Unveil Outstanding Users Ranking with Public Attainable Features
Author
Li, Wanda (Fudan University)
Xu, Zhiwei (Fudan University)
Sun, Yi (Fudan University)
Gong, Qingyuan (Fudan University)
Chen, Y. (Fudan University)
Ding, Aaron Yi (TU Delft Information and Communication Technology) 
Wang, Xin (Fudan University)
Hui, Pan (The Hong Kong University of Science and Technology; University of Helsinki)
Date
2023
Abstract
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.
Subject
Bridges
Computer science
Deep Neural Networks
Feature extraction
Integrated circuit modeling
Neural networks
Online Social Networks
Outstanding User Detection
Social networking (online)
Task analysis
To reference this document use:
http://resolver.tudelft.nl/uuid:1280a3c4-b32b-4019-9903-c82996848040
DOI
https://doi.org/10.1109/TKDE.2021.3091503
Embargo date
2023-07-01
ISSN
1041-4347
Source
IEEE Transactions on Knowledge & Data Engineering, 35 (1), 291-306
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care 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.
Part of collection
Institutional Repository
Document type
journal article
Rights
© 2023 Wanda Li, Zhiwei Xu, Yi Sun, Qingyuan Gong, Y. Chen, Aaron Yi Ding, Xin Wang, Pan Hui