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Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks.

Title: Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks.
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Name(s): Zhu, Junxing, author
Zhang, Jiawei, author
Wu, Quanyuan, author
Jia, Yan, author
Zhou, Bin, author
Wei, Xiaokai, author
Yu, Philip S, author
Type of Resource: text
Genre: Journal Article
Text
Date Issued: 2017-08-03
Physical Form: computer
online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: Nowadays, 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 on anchor links, if an unlabeled anchor link a = ( u , v ) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account or account will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed method can outperform other methods with significant advantages.
Identifier: FSU_pmch_28771201 (IID), 10.3390/s17081786 (DOI), PMC5580167 (PMCID), 28771201 (RID), 28771201 (EID), s17081786 (PII)
Keywords: Constrained Active Learning, Anchor link prediction, Multiple heterogeneous social networks
Publication Note: This NIH-funded author manuscript originally appeared in PubMed Central at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5580167.
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_pmch_28771201
Owner Institution: FSU
Is Part Of: Sensors (Basel, Switzerland).
1424-8220
Issue: iss. 8, vol. 17

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Zhu, J., Zhang, J., Wu, Q., Jia, Y., Zhou, B., Wei, X., & Yu, P. S. (2017). Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks. Sensors (Basel, Switzerland). Retrieved from http://purl.flvc.org/fsu/fd/FSU_pmch_28771201