Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 4
1080-1085

Preserving link prediction similarity matrix for attribute network embedding

Wu Jiehua1
Gao Xueqin1
Wang Tao2
1. Dept. of Computer Science & Engineering, Guangdong Polytechnic of Industry & Commerce, Guangzhou 510510, China
2. School of Computer Science, South China Normal University, Guangzhou 510631, China
DOI: 10.19734/j.issn.1001-3695.2021.07.0377

Abstract

In attribute network, the attribute information associated with nodes is essential to improve the performance of network embedding tasks. Nevertheless, network is a graph structure, in which nodes not only contain attribute information but also embrace the rich structural information. In order to make full use of the structural information, firstly, this paper defined influential node characteristics, spatial relationships, and constructed similarity matrix based on the definition of link prediction. Then it mapped correlation similarity vector associated with nodes in the binary group to the relationship space of the adjacency matrix, so as to maintain the node vector matrix structure information feature. Based on the definition of normalized graph Laplacian, it fused the attribute information and label feature and integrated the above three kinds of information into an optimization framework. Finally, it inferenced the projection matrix by calculating the local maximum value through a second order derivative. Experimental results indicate that the proposed algorithm can effectively utilize information of the adjacency structure with the binary group of nodes, and compared with other benchmark network embedding algorithms, it also can achieve better results on the node classification task.

Foundation Support

广东省自然科学基金资助项目(2020A1515011495)
广州市基础与应用基础研究项目(202002030266)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.07.0377
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 4
Section: Algorithm Research & Explore
Pages: 1080-1085
Serial Number: 1001-3695(2022)04-021-1080-06

Publish History

[2021-11-22] Accepted Paper
[2022-04-05] Printed Article

Cite This Article

伍杰华, 高学勤, 王涛. 融合链接预测相似度矩阵的属性网络嵌入算法 [J]. 计算机应用研究, 2022, 39 (4): 1080-1085. (Wu Jiehua, Gao Xueqin, Wang Tao. Preserving link prediction similarity matrix for attribute network embedding [J]. Application Research of Computers, 2022, 39 (4): 1080-1085. )

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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