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Entropy 2019, 21(3), 254; https://doi.org/10.3390/e21030254

Anchor Link Prediction across Attributed Networks via Network Embedding

1
Guanghua School of Management, Peking University, Beijing 100871, China
2
Harvest Fund Management Co., Ltd., Beijing 100005, China
3
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China
4
Tencent, Shenzhen 518057, China
5
Department of Information Systems, City University of Hong Kong, Kowloon Tong, Hong Kong, China
6
School of Information Engineering Department, East China Jiaotong University, Nanchang 330013, China
7
College of Computer Science, Beijing University of Technology, Beijing 100124, China
*
Author to whom correspondence should be addressed.
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)
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Abstract

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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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.

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