Cross View Link Prediction by Learning Noise-resilient Representation Consensus
Cross View Link Prediction by Learning Noise-resilient Representation Consensus
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DOI：
10.1145/3038912.3052575
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发表时间：
2017-04
期刊：
影响因子：
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通讯作者：
Xiaokai Wei;Linchuan Xu;Bokai Cao;Philip S. Yu
中科院分区：
文献类型：
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作者：
Xiaokai Wei;Linchuan Xu;Bokai Cao;Philip S. Yu
链接预测一直是社会网络和信息网络的重要任务。现有的方法通常假设网络结构的完整性。然而，在许多现实世界的网络中，链接和节点属性通常是部分可观察的。在本文中，我们研究了部分可观察网络上的跨视图链接预测（CVLP）问题，重点是将只有链接的节点推荐给只有属性的节点（反之亦然）。我们的目标是通过学习一个强大的共识，基于链接和基于属性的表示，使节点成为潜在空间中的可比信息差距。此外，基于链接的表示和基于属性的表示可以通过这种共识学习相互增强。此外，属性选择与表示学习联合进行，以减轻噪声高维属性的影响。我们提出了这个框架的两个实例与不同的损失函数，并开发了一个交替的优化框架来解决这个问题。在四个真实数据集上的实验结果表明，该算法在跨视图链接预测方面明显优于基准方法。
Link Prediction has been an important task for social and information networks. Existing approaches usually assume the completeness of network structure. However, in many real-world networks, the links and node attributes can usually be partially observable. In this paper, we study the problem of Cross View Link Prediction (CVLP) on partially observable networks, where the focus is to recommend nodes with only links to nodes with only attributes (or vice versa). We aim to bridge the information gap by learning a robust consensus for link-based and attribute-based representations so that nodes become comparable in the latent space. Also, the link-based and attribute-based representations can lend strength to each other via this consensus learning. Moreover, attribute selection is performed jointly with the representation learning to alleviate the effect of noisy high-dimensional attributes. We present two instantiations of this framework with different loss functions and develop an alternating optimization framework to solve the problem. Experimental results on four real-world datasets show the proposed algorithm outperforms the baseline methods significantly for cross-view link prediction.