Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks
AbstractNowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link
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Zhu, J.; Zhang, J.; Wu, Q.; Jia, Y.; Zhou, B.; Wei, X.; Yu, P.S. Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks. Sensors 2017, 17, 1786.
Zhu J, Zhang J, Wu Q, Jia Y, Zhou B, Wei X, Yu PS. Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks. Sensors. 2017; 17(8):1786.Chicago/Turabian Style
Zhu, Junxing; Zhang, Jiawei; Wu, Quanyuan; Jia, Yan; Zhou, Bin; Wei, Xiaokai; Yu, Philip S. 2017. "Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks." Sensors 17, no. 8: 1786.
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