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Restricted Boltzmann Machine-Based Approaches for Link Prediction in Dynamic Networks
Li, TS (Li, Taisong)[ 1,2 ]; Wang, B (Wang, Bing)[ 1 ]; Jiang, YS (Jiang, Yasong)[ 1 ]; Zhang, Y (Zhang, Yan)[ 1 ]; Yan, YH (Yan, Yonghong)[ 1,2,3 ]
2018
Source PublicationIEEE ACCESS
ISSN2169-3536
Volume6Issue:6Pages:29940-29951
Abstract

Link prediction in dynamic networks aims to predict edges according to historical linkage status. It is inherently difficult because of the linear/non-linear transformation of underlying structures. The problem of efficiently performing dynamic link inference is extremely challenging due to the scale of networks and different evolving patterns. Most previous approaches for link prediction are based on members' similarity and supervised learning methods. However, research work on investigating hidden patterns of dynamic social networks is rarely conducted. In this paper, we propose a novel framework that incorporates a deep learning method, i.e., temporal restricted Boltzmann machine, and a machine learning approach, i.e., gradient boosting decision tree. The proposed model is capable of modeling each link's evolving patterns. We also propose a novel transformation for input matrix, which significantly reduces the computational complexity and makes our algorithm scalable to large networks. Extensive experiments demonstrate that the proposed method outperforms the existing state-of-the-art algorithms on real-world dynamic networks.

KeywordLink Prediction Social Network Analysis Deep Learning
DOI10.1109/ACCESS.2018.2840054
Indexed BySCI
WOS IDWOS:000435522600042
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.xjipc.cas.cn/handle/365002/5637
Collection多语种信息技术研究室
Affiliation1.Chinese Acad Sci, Inst Acoust, Key Lab Speech Acoust & Content Understanding, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Dept Phys, Beijing 101408, Peoples R China
3.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Xinjiang Key Lab Minor Speech & Language Informat, Urumqi 830011, Peoples R China
Recommended Citation
GB/T 7714
Li, TS ,Wang, B ,Jiang, YS ,et al. Restricted Boltzmann Machine-Based Approaches for Link Prediction in Dynamic Networks[J]. IEEE ACCESS,2018,6(6):29940-29951.
APA Li, TS ,Wang, B ,Jiang, YS ,Zhang, Y ,&Yan, YH .(2018).Restricted Boltzmann Machine-Based Approaches for Link Prediction in Dynamic Networks.IEEE ACCESS,6(6),29940-29951.
MLA Li, TS ,et al."Restricted Boltzmann Machine-Based Approaches for Link Prediction in Dynamic Networks".IEEE ACCESS 6.6(2018):29940-29951.
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