A Shared Interest Discovery Model for Coauthor Relationship in SNS
A social network service (SNS) is a platform to build social networks or social relations among people. Many users enjoy SNS with their smart devices, which are mostly equipped with sensory devices. The sensitive information produced by these sensory devices is uploaded to SNS, which may raise many...
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2014-04-01
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Series: | International Journal of Distributed Sensor Networks |
Online Access: | https://doi.org/10.1155/2014/820715 |
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doaj-86e72b1a754940bbab8024ccc61074502020-11-25T03:27:19ZengSAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772014-04-011010.1155/2014/820715820715A Shared Interest Discovery Model for Coauthor Relationship in SNSXin An0Shuo Xu1Yali Wen2Mingxing Hu3 School of Economics and Management, Beijing Forestry University, No. 35 Qinghua East Road, Haidian District, Beijing 100083, China Information Technology Supporting Center, Institute of Scientific and Technical Information of China, No. 15 fuxing Road, Haidian District, Beijing 100038, China School of Economics and Management, Beijing Forestry University, No. 35 Qinghua East Road, Haidian District, Beijing 100083, China School of Economics and Management, Beijing Forestry University, No. 35 Qinghua East Road, Haidian District, Beijing 100083, ChinaA social network service (SNS) is a platform to build social networks or social relations among people. Many users enjoy SNS with their smart devices, which are mostly equipped with sensory devices. The sensitive information produced by these sensory devices is uploaded to SNS, which may raise many potential risks. In order to share one's sensitive data with random people without security and privacy concerns, this paper proposes a shared interest discovery model for coauthor relationship in SNS, named as coauthor topic (coAT) model, to identify the users with similar interests from social networks, and collapsed Gibbs sampling method is utilized for inferring model parameters. Thus, one can reduce the possibility that recommended users are not friends but attackers. Finally, extensive experimental results on NIPS dataset indicate that our coAT model is feasible and efficient.https://doi.org/10.1155/2014/820715 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xin An Shuo Xu Yali Wen Mingxing Hu |
spellingShingle |
Xin An Shuo Xu Yali Wen Mingxing Hu A Shared Interest Discovery Model for Coauthor Relationship in SNS International Journal of Distributed Sensor Networks |
author_facet |
Xin An Shuo Xu Yali Wen Mingxing Hu |
author_sort |
Xin An |
title |
A Shared Interest Discovery Model for Coauthor Relationship in SNS |
title_short |
A Shared Interest Discovery Model for Coauthor Relationship in SNS |
title_full |
A Shared Interest Discovery Model for Coauthor Relationship in SNS |
title_fullStr |
A Shared Interest Discovery Model for Coauthor Relationship in SNS |
title_full_unstemmed |
A Shared Interest Discovery Model for Coauthor Relationship in SNS |
title_sort |
shared interest discovery model for coauthor relationship in sns |
publisher |
SAGE Publishing |
series |
International Journal of Distributed Sensor Networks |
issn |
1550-1477 |
publishDate |
2014-04-01 |
description |
A social network service (SNS) is a platform to build social networks or social relations among people. Many users enjoy SNS with their smart devices, which are mostly equipped with sensory devices. The sensitive information produced by these sensory devices is uploaded to SNS, which may raise many potential risks. In order to share one's sensitive data with random people without security and privacy concerns, this paper proposes a shared interest discovery model for coauthor relationship in SNS, named as coauthor topic (coAT) model, to identify the users with similar interests from social networks, and collapsed Gibbs sampling method is utilized for inferring model parameters. Thus, one can reduce the possibility that recommended users are not friends but attackers. Finally, extensive experimental results on NIPS dataset indicate that our coAT model is feasible and efficient. |
url |
https://doi.org/10.1155/2014/820715 |
work_keys_str_mv |
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