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Erschienen in: World Wide Web 1/2019

25.04.2018

Worship prediction: identify followers in celebrity-dived networks

verfasst von: Shan-Yun Teng, Lo-Pang-Yun Ting, Mi-Yen Yeh, Kun-Ta Chuang

Erschienen in: World Wide Web | Ausgabe 1/2019

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Abstract

We in this paper explore a new link prediction paradigm, called ‘worship’ prediction, to discover worship links between users and celebrities on social networks. The prediction of ‘worship’ links enables valuable social services, such as viral marketing, popularity estimation, and celebrity recommendation. However, as the concern of business security and personal privacy, only public-accessible statistical social properties, instead of the detailed information of users, can be utilized to predict the ‘worship’ labels. In addition, we observe that friendship properties are not effective to predict the desired links, meaning that most of previous work which rely on the friendship properties cannot be successfully applied in the prediction of worship link. To address these issues, a novel learning framework is devised, including a factor graph with new discovered statistical properties and a Gaussian estimation based learning algorithm with active learning. Our experimental studies on real data, including Instagram, Twitter and DBLP, show that the proposed learning framework can overcome the problem of missing labels and efficiently discover worship links.

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Metadaten
Titel
Worship prediction: identify followers in celebrity-dived networks
verfasst von
Shan-Yun Teng
Lo-Pang-Yun Ting
Mi-Yen Yeh
Kun-Ta Chuang
Publikationsdatum
25.04.2018
Verlag
Springer US
Erschienen in
World Wide Web / Ausgabe 1/2019
Print ISSN: 1386-145X
Elektronische ISSN: 1573-1413
DOI
https://doi.org/10.1007/s11280-018-0569-y

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