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