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中南大学学报(英文版)

Journal of Central South University

Vol. 26    No. 10    October 2019

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Tri-party deep network representation learning using inductive matrix completion
YE Zhong-lin(冶忠林)1, 2, 3, ZHAO Hai-xing(赵海兴)1, 2, 3, 4, ZHANG Ke(张科)1, 2, 3, ZHU Yu(朱宇)1, 2, 3, XIAO Yu-zhi(肖玉芝)1, 2, 3

1. College of Computer, Qinghai Normal University, Xining 810008, China;
2. Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province,Xining 810008, China;
3. Key Laboratory of Tibetan Information Processing of Ministry of Education, Xining 810008, China;
4. College of Computer Science, Shaanxi Normal University, Xi’an 130012, China;

Abstract:Most existing network representation learning algorithms focus on network structures for learning. However, network structure is only one kind of view and feature for various networks, and it cannot fully reflect all characteristics of networks. In fact, network vertices usually contain rich text information, which can be well utilized to learn text-enhanced network representations. Meanwhile, Matrix-Forest Index (MFI) has shown its high effectiveness and stability in link prediction tasks compared with other algorithms of link prediction. Both MFI and Inductive Matrix Completion (IMC) are not well applied with algorithmic frameworks of typical representation learning methods. Therefore, we proposed a novel semi-supervised algorithm, tri-party deep network representation learning using inductive matrix completion (TDNR). Based on inductive matrix completion algorithm, TDNR incorporates text features, the link certainty degrees of existing edges and the future link probabilities of non-existing edges into network representations. The experimental results demonstrated that TFNR outperforms other baselines on three real-world datasets. The visualizations of TDNR show that proposed algorithm is more discriminative than other unsupervised approaches.

 

Key words: network representation; network embedding; representation learning; matrix-forestindex; inductive matrix completion

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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