Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 6
1708-1714

Truss community search based on keyword attributes over heterogeneous networks

Yang Chengbo1
Zhou Lihua1
Huang Yaqun1
Yang Yudi2
1. School of Information Science & Engineering, Yunnan University, Kunming 650500, China
2. Dianchi College of Yunnan University, Kunming 650228, China
DOI: 10.19734/j.issn.1001-3695.2022.10.0512

Abstract

Community search, as an important research content of social network analysis, aims to find densely connected subgraphs that highly relate to the query node given by users. Most community search methods currently available focus on homogeneous networks. However, in reality, information networks are often attribute-heterogeneous. This paper proposed P-distance and S-distance based on meta-path P and meta-structure S in heterogeneous networks, as well as(k, d, P) -truss and(k, d, S) -truss community models, to measure the structural cohesion of subgraphs. Additionally, it proposed a keyword attribute score function to measure the keyword attribute correlation of different subgraphs, and presented algorithms which could find communities with the highest keyword attribute score of(k, d, P) -truss and(k, d, S) -truss. Search algorithms could find a personalized community with both structural cohesion and keyword attribute correlation, and support to limit the maximum distance d between the query node and any node in the community to control the scope of community search. Compared with the related community search algorithms on real-world datasets, the experimental results prove the effectiveness and feasibility of the proposed algorithms.

Foundation Support

国家自然科学基金资助项目(62062066,61762090,61966036,62276227)
云南省基础研究计划重点资助项目(202201AS070015)
云南省智能系统与计算重点实验室资助项目(202205AG070003)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.10.0512
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 6
Section: Algorithm Research & Explore
Pages: 1708-1714
Serial Number: 1001-3695(2023)06-017-1708-07

Publish History

[2023-01-13] Accepted Paper
[2023-06-05] Printed Article

Cite This Article

杨成波, 周丽华, 黄亚群, 等. 异质网络中基于关键词属性的Truss社区搜索 [J]. 计算机应用研究, 2023, 40 (6): 1708-1714. (Yang Chengbo, Zhou Lihua, Huang Yaqun, et al. Truss community search based on keyword attributes over heterogeneous networks [J]. Application Research of Computers, 2023, 40 (6): 1708-1714. )

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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