IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE) 
2021

Impact Factor: 7.251

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

Abstract:

Outstanding users (OUs) denote the influential, "core" or "bridge" 
users in the online community. 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 network's information or 
at least ego networks. 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 nonlinear features. For specialization, 
we leverage the traditional well-defined metrics to preserve 
common features. Extensive experiments based on real-world 
datasets demonstrate that our approach achieves a high efficacy in 
terms of detection performance against the state-of-the-art.


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BibTeX:
@article{Li:TKDE2021,
title = "DeepPick: A Deep Learning Approach to Unveil Outstanding Users Ranking with Public Attainable Features",
journal = "IEEE Transactions on Knowledge and Data Engineering",
year = "2021",
author = "Wanda Li, Zhiwei Xu, Yi Sun, Qingyuan Gong, Yang Chen, Aaron Yi Ding, Xin Wang, Pan Hui",
}
How to cite:

Wanda Li, Zhiwei Xu, Yi Sun, Qingyuan Gong, Yang Chen, Aaron Yi Ding, Xin Wang, Pan Hui, "DeepPick: A Deep Learning Approach to Unveil Outstanding Users Ranking with Public Attainable Features", in IEEE Transactions on Knowledge and Data Engineering, 2021.