Predicting Social Links for New Users across Aligned Heterogeneous Social Networks
Predicting Social Links for New Users across Aligned Heterogeneous Social Networks
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DOI：
10.1109/icdm.2013.134
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发表时间：
2013-10
期刊：
影响因子：
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通讯作者：
Jiawei Zhang;Xiangnan Kong;Philip S. Yu
中科院分区：
文献类型：
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作者：
Jiawei Zhang;Xiangnan Kong;Philip S. Yu
如今，每天都有许多新用户不断加入到在线社交网络中，这些新用户通常社交关系很少，网络中的辅助信息也非常稀疏。预测新用户的社交链接非常重要。与传统的链路预测问题不同，由于缺乏来自网络中新用户的信息，新用户的链路预测更具挑战性。同时，近年来用户通常同时参与多个社交网络，以享受不同社交网络提供的特定服务。多个网络的共享用户可以作为与其所参与的网络对齐的“锚”。在本文中，我们提出了一种名为SCAN-PS （Supervised Cross - Aligned Networks link prediction with Personalized Sampling）的链接预测方法来解决新用户的社交链接预测问题。SCAN-PS可以使用通过对齐帐户从目标网络和其他源网络中的现有活动用户传输的信息。此外，SCAN-PS还可以解决目标网络中完全没有这些新用户信息时的冷启动问题。在两个真实的异构社交网络上进行的大量实验表明，SCAN-PS可以很好地预测新用户的社交链接。
Nowadsys, many new users are keeping joining in the online social networks every day and these new users usually have very few social connections and very sparse auxiliary information in the network. Prediction social links for new users is very important. Different from conventional link prediction problems, link prediction for new users is more challenging due to the lack of information from the new users in the network. Meanwhile, in recent years, users are usually involved in multiple social networks simultaneously to enjoy the specific services offered by different social networks. The shared users of multiple networks can act as the "anchors" aligned the networks they participate in. In this paper, we propose a link prediction method called SCAN-PS (Supervised Cross Aligned Networks link prediction with Personalized Sampling), to solve the social link prediction problem for new users. SCAN-PS can use information transferred from both the existing active users in the target network and other source networks through aligned accounts. In addition, SCAN-PS could solve the cold start problem when information of these new users is total absent in the target network. Extensive experiments conducted on two real-world aligned heterogeneous social networks demonstrate that SCAN-PS can perform well in predicting social links for new users.