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Anchor Link Prediction across Attributed Networks via Network Embedding

Guanghua School of Management, Peking University, Beijing 100871, China
Harvest Fund Management Co., Ltd., Beijing 100005, China
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China
Tencent, Shenzhen 518057, China
Department of Information Systems, City University of Hong Kong, Kowloon Tong, Hong Kong, China
School of Information Engineering Department, East China Jiaotong University, Nanchang 330013, China
College of Computer Science, Beijing University of Technology, Beijing 100124, China
Author to whom correspondence should be addressed.
Entropy 2019, 21(3), 254;
Received: 24 January 2019 / Revised: 23 February 2019 / Accepted: 2 March 2019 / Published: 6 March 2019
(This article belongs to the Special Issue Complex Networks from Information Measures)
Presently, many users are involved in multiple social networks. Identifying the same user in different networks, also known as anchor link prediction, becomes an important problem, which can serve numerous applications, e.g., cross-network recommendation, user profiling, etc. Previous studies mainly use hand-crafted structure features, which, if not carefully designed, may fail to reflect the intrinsic structure regularities. Moreover, most of the methods neglect the attribute information of social networks. In this paper, we propose a novel semi-supervised network-embedding model to address the problem. In the model, each node of the multiple networks is represented by a vector for anchor link prediction, which is learnt with awareness of observed anchor links as semi-supervised information, and topology structure and attributes as input. Experimental results on the real-world data sets demonstrate the superiority of the proposed model compared to state-of-the-art techniques. View Full-Text
Keywords: anchor link prediction; network embedding; attributed network anchor link prediction; network embedding; attributed network
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MDPI and ACS Style

Wang, S.; Li, X.; Ye, Y.; Feng, S.; Lau, R.Y.K.; Huang, X.; Du, X. Anchor Link Prediction across Attributed Networks via Network Embedding. Entropy 2019, 21, 254.

AMA Style

Wang S, Li X, Ye Y, Feng S, Lau RYK, Huang X, Du X. Anchor Link Prediction across Attributed Networks via Network Embedding. Entropy. 2019; 21(3):254.

Chicago/Turabian Style

Wang, Shaokai, Xutao Li, Yunming Ye, Shanshan Feng, Raymond Y.K. Lau, Xiaohui Huang, and Xiaolin Du. 2019. "Anchor Link Prediction across Attributed Networks via Network Embedding" Entropy 21, no. 3: 254.

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