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Open AccessArticle

Learning Effective Feature Representation against User Privacy Protection on Social Networks

by 1,* and 2
1
Institute of Data Science, National Cheng Kung University, Tainan 70101, Taiwan
2
Department of Statistics, National Cheng Kung University, Tainan 70101, Taiwan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(14), 4835; https://doi.org/10.3390/app10144835
Received: 7 June 2020 / Revised: 6 July 2020 / Accepted: 10 July 2020 / Published: 14 July 2020
(This article belongs to the Section Computing and Artificial Intelligence)
Users pay increasing attention to their data privacy in online social networks, resulting in hiding personal information, such as profile attributes and social connections. While network representation learning (NRL) is widely effective in social network analysis (SNA) tasks, it is essential to learn effective node embeddings from privacy-protected sparse and incomplete network data. In this work, we present a novel NRL model to generate node embeddings that can afford data incompleteness coming from user privacy protection. We propose a structure-attribute enhanced matrix (SAEM) to alleviate data sparsity and develop a community-cluster informed NRL method, c2n2v, to further improve the quality of embedding learning. Experiments conducted across three datasets, three simulations of user privacy protection, and three downstream SNA tasks exhibit the promising performance of our NRL model SAEM+c2n2v. View Full-Text
Keywords: feature representation learning; node embeddings; privacy protection; social networks feature representation learning; node embeddings; privacy protection; social networks
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MDPI and ACS Style

Li, C.-T.; Zeng, Z.-Y. Learning Effective Feature Representation against User Privacy Protection on Social Networks. Appl. Sci. 2020, 10, 4835. https://doi.org/10.3390/app10144835

AMA Style

Li C-T, Zeng Z-Y. Learning Effective Feature Representation against User Privacy Protection on Social Networks. Applied Sciences. 2020; 10(14):4835. https://doi.org/10.3390/app10144835

Chicago/Turabian Style

Li, Cheng-Te; Zeng, Zi-Yun. 2020. "Learning Effective Feature Representation against User Privacy Protection on Social Networks" Appl. Sci. 10, no. 14: 4835. https://doi.org/10.3390/app10144835

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