Algorithm Research & Explore
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3316-3321

Deep attributed bipartite network embedding incorporating with multilevel structure information

Li Tingting1a,1b
Lyu Shaoqing1a,1b
Zhao Xueli1a,1b
Ren Xincheng2
1. a. School of Communications & Information Engineering, b. Shaanxi Key Laboratory of Information Communication Network & Security, Xi'an University of Posts & Telecommunications, Xi'an 710121, China
2. Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data, Yan'an University, Yan'an Shaanxi 716000, China

Abstract

Network representation learning aims to transform the nodes in the network into a low-dimensional vector space while maintaining the inherent properties of the network. Most of the existing methods are aimed at normal networks, ignoring the particularity of attribute bipartite networks and the highly non-linear characteristics of the network. To solve the above problem, this paper proposed a deep attributed bipartite network embedding method incorporating with multilevel structure information. Specifically, the algorithm introduced an extended weight matrix to fuse the explicit and implicit structure of the bipartite network with attribute information. Then, it utilized a deep auto-encoder model to capture the highly non-linear characteristics of the network. To maintain the global network structure, a deep auto-encoder reconstructed the second-order proximity. Meanwhile, the deep auto-encoder used the first-order proximity of the nodes as supervisory information to maintain the local network structure. Finally, the algorithm performed joint optimization to get the final representation vectors of nodes. The model was executed on the four datasets of Yelp, Douban Book, Douban Movie and MovieLens. Compared with the latest benchmark method, the average values of F1@10, MAP@10, MRR@10 and NDGG@10 of this model have improved by 4.29%、5.63%、6.26%、4.21%.

Foundation Support

陕西省能源大数据智能处理省市共建重点实验室开放基金项目(IPBED10)
陕西省工业领域一般项目(2020GY-081)
陕西省重点研发计划项目(2018ZDXM-GY-041)
陕西省教育厅科研计划项目(17JK0703)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.04.0104
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 11
Section: Algorithm Research & Explore
Pages: 3316-3321
Serial Number: 1001-3695(2021)11-020-3316-06

Publish History

[2021-11-05] Printed Article

Cite This Article

李婷婷, 吕少卿, 赵雪莉, 等. 融合多层次结构信息的深度属性二分网络表示学习 [J]. 计算机应用研究, 2021, 38 (11): 3316-3321. (Li Tingting, Lyu Shaoqing, Zhao Xueli, et al. Deep attributed bipartite network embedding incorporating with multilevel structure information [J]. Application Research of Computers, 2021, 38 (11): 3316-3321. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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