Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 2
424-429,442

Link prediction based on matrix factorization for DeepWalk

Ye Zhonglin1,2,3
Cao Rong2,3,4
Zhao Haixing1,2,3,4
Zhang Ke2,3,4
Zhu Yu2,3,4
1. College of Computer Science, Shaanxi Normal University, Xi'an 710119, China
2. Tibetan Information Processing & 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, Qinghai Normal University, Xining 810008, China
DOI: 10.19734/j.issn.1001-3695.2018.07.0523

Abstract

The data sources of existing link prediction algorithms are mainly based on neighbors, paths, and random walk methods. The link prediction algorithms use mainly node similarity assumptions or maximum likelihood estimates. The link prediction based on neural network is still absent. Some research achievements based on neural network show that the DeepWalk algorithm based on neural network is an efficient network representation learning algorithm, which can more effectively learn the network structure features in the network. It has been proven that DeepWalk is equivalent to factorize the target matrix. Therefore, this paper proposed a link prediction algorithm(LPMF) based on matrix factorization of DeepWalk. This algorithm based on matrix factorization used the DeepWalk algorithm to get the network representation vectors. And then, it calculated the similarities between node pairs of nodes by the cosine similarity method. Based on that, the similarity matrix of the target network was constructed. Finally, this paper used the similarity matrix to conduct the link prediction experiments on three real-world citation networks. The experimental results show that the new method is superior to the existing 20 kinds of link prediction algorithms, which fully shows that LPMF can effectively find the structural correlation between nodes in the network, and performs a more excellent performance in the actual tasks of link prediction.

Foundation Support

NSFC支持项目(11661069,61763041)
国家教育部长江学者和创新团队发展计划资助项目(IRT_15R40)
青海省自然科学基金资助项目(2013-Z-Y17,2014-ZJ-721)
中央高校基本科研基金资助项目(2017TS045)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.07.0523
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 2
Section: Algorithm Research & Explore
Pages: 424-429,442
Serial Number: 1001-3695(2020)02-022-0424-06

Publish History

[2020-02-05] Printed Article

Cite This Article

冶忠林, 曹蓉, 赵海兴, 等. 基于矩阵分解的DeepWalk链路预测算法 [J]. 计算机应用研究, 2020, 37 (2): 424-429,442. (Ye Zhonglin, Cao Rong, Zhao Haixing, et al. Link prediction based on matrix factorization for DeepWalk [J]. Application Research of Computers, 2020, 37 (2): 424-429,442. )

About the Journal

  • 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.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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