DeepPick

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

Journal article (2023)

Authors

Wanda Li Fudan University
Zhiwei Xu Fudan University
Yi Sun Fudan University
Qingyuan Gong Fudan University
Y. Chen Fudan University
Xin Wang Fudan University
Pan Hui The Hong Kong University of Science and Technology, University of Helsinki
Copyright: © 2023 Wanda Li, Zhiwei Xu, Yi Sun, Qingyuan Gong, Y. Chen, Aaron Yi Ding, Xin Wang, Pan Hui
DOI
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https://doi.org/10.1109/TKDE.2021.3091503
Deep Neural Networks Feature extraction Computer science Neural networks Bridges Integrated circuit modeling Task analysis Social networking (online) Online Social Networks Outstanding User Detection
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Published Date

2023

Language

English

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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.