Print Email Facebook Twitter DeepPick 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 BridgesComputer scienceDeep Neural NetworksFeature extractionIntegrated circuit modelingNeural networksOnline Social NetworksOutstanding User DetectionSocial 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 Files PDF DeepPick_A_Deep_Learning_ ... atures.pdf 1.42 MB Close viewer /islandora/object/uuid:1280a3c4-b32b-4019-9903-c82996848040/datastream/OBJ/view