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Tagging Users’ Social Circles via Multiple Linear Regression

Harbin Institute of Technology, Harbin 150001, China
Microsoft Research Asia, Beijing 100086, China
Author to whom correspondence should be addressed.
Academic Editor: Remo Pareschi
Informatics 2016, 3(3), 10;
Received: 4 March 2016 / Revised: 12 April 2016 / Accepted: 24 May 2016 / Published: 24 June 2016
(This article belongs to the Special Issue Social Computing for Knowledge Management)
A social circle is a category of strong social relationships, such as families, classmates and good friends and so on. The information diffusion among members of online social circles is frequent and credible. The research of users’ online social circles has become popular in recent years. Many scholars propose methods for detecting users’ online social circles. On the other hand, the social meanings and the tags of a social circle are also important for the analysis of a social circle. However, little work involves the tags discovery of social circles. This paper proposes an algorithm for social circle tag detection by multiple linear regression. The model solves the data sparse problem of tags in social circles and successfully combines different categories of features in social circles. We also redmap the concept of the social circle into "reference circles" of an academic paper. We evaluate our method in datasets of both Facebook and Microsoft Academic Search, and prove that it is more effective than other relevant methods. View Full-Text
Keywords: tag detection; social circle; multiple linear regression tag detection; social circle; multiple linear regression
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MDPI and ACS Style

Qin, H.; Liu, J.; Lin, C.-Y.; Liu, T. Tagging Users’ Social Circles via Multiple Linear Regression. Informatics 2016, 3, 10.

AMA Style

Qin H, Liu J, Lin C-Y, Liu T. Tagging Users’ Social Circles via Multiple Linear Regression. Informatics. 2016; 3(3):10.

Chicago/Turabian Style

Qin, Hailong, Jing Liu, Chin-Yew Lin, and Ting Liu. 2016. "Tagging Users’ Social Circles via Multiple Linear Regression" Informatics 3, no. 3: 10.

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