Cross View Link Prediction by Learning Noise-resilient Representation Consensus

By Xiaokai Wei, Linchuan Xu, Bokai Cao, Philip S. Yu. 2017

86 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chuan Shi
2. Xiao Wang
3. Philip S. Yu
4. Peng Cui
5. Wenwu Zhu
6. Jian Pei
7. Binbin Hu
8. Wayne Xin Zhao

In Statistical and Nonlinear Physics

1. Chuan Shi
2. Xiao Wang
3. Peng Cui
4. Wenwu Zhu
5. Philip S. Yu
6. Jian Pei
7. Binbin Hu
8. Wayne Xin Zhao

Citations in the corpus

• Heterogeneous Information Network Embedding for Recommendation (2017, methodology): “On the other hand, network embedding has shown its potential in structure feature extraction and has been successfully applied in many data mining tasks [39], [40], [41], [42], such as classification [43], clustering [44], [45] and recommendation [46], [47].”
• A Survey on Network Embedding (2017, background): “[57] study the problem of cross view link pre-”