计算机科学

计算机科学 ›› 2022, Vol. 49 ›› Issue (8): 97-107.doi: 10.11896/jsjkx.210700202

• 数据库&大数据&数据科学* • 上一篇    下一篇

基于顶点粒k步搜索和粗糙集的强连通分量挖掘算法

程富豪1, 徐泰华1, 陈建军1, 宋晶晶1,2, 杨习贝1   

  1. 1 江苏科技大学计算机学院 江苏 镇江 212000
    2 数据科学与智能应用福建省高校重点实验室 福建 漳州 363000
  • 收稿日期:2021-07-20 修回日期:2021-10-23 发布日期:2022-08-02
  • 通讯作者: 徐泰华(xth19890410@163.com)
  • 作者简介:(cfh_vip@163.com)
  • 基金资助:
    国家自然科学基金(62006099,62076111,61906078);江苏省高等学校自然科学基金(20KJB520010);镇江市重点研发计划——社会发展(SH2018005)

Strongly Connected Components Mining Algorithm Based on k-step Search of Vertex Granule and Rough Set Theory

CHENG Fu-hao1, XU Tai-hua1, CHEN Jian-jun1, SONG Jing-jing1,2, YANG Xi-bei1   

  1. 1 School of Computer,Jiangsu University of Science and Technology,Zhenjiang,Jiangsu 212000,China
    2 Key Laboratory of Data Science and Intelligent Application,Fujian Province University,Zhangzhou,Fujian 363000,China
  • Received:2021-07-20 Revised:2021-10-23 Published:2022-08-02
  • About author:CHENG Fu-hao,born in 1994, postgraduate.His main research interests include granular computing and graph theory.
    XU Tai-hua,born in 1989,Ph.D,lecture,master supervisor,is a member of China Computer Federation.His main research interests include intelligent information processing,granular computing and graph theory.
  • Supported by:
    National Natural Science Foundation of China(62006099,62076111,61906078),Natural Science Foundation of Jiangsu Provincial Colleges and Universities(20KJB520010) and Key Research and Development Plan of Zhenjiang City－Social Development(SH2018005).

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摘要： 强连通分量挖掘是图论中的经典问题之一,如何设计更高效率的串行强连通分量挖掘算法具有现实需求。GRSCC算法利用k步上近似和k步R相关集这两个粗糙集算子所构成的SUB-RSCC函数,可实现简单有向图中的强连通分量挖掘,而SUB-RSCC函数的调用次数决定了挖掘效率。根据挖掘强连通分量时顶点间存在的相关性,GRSCC算法引入了粒化策略,减少了SUB-RSCC函数的调用次数,提高了挖掘效率。在GRSCC算法的基础上,分析发现了顶点间的另外两种强连通分量相关性,由此设计了一种新的顶点粒化策略,进而提出了一种顶点粒k步搜索方法,可更大程度地减少SUB-RSCC函数的调用次数。最后,提出了一种基于顶点粒k步搜索和粗糙集的强连通分量挖掘算法KGRSCC。实验结果表明,相比RSCC算法、GRSCC算法和Tarjan算法,KGRSCC算法具有更好的性能。

关键词: 粗糙集, 顶点粒k步搜索, 粒化策略, 强连通分量, 图论

Abstract: Strong connected components (SCCs) mining is one of the classic problems in graph theory.It has practical requirements to design a serial SCCs mining algorithm with high efficiency.GRSCC algorithm can use SUB-RSCC function to discover SCCs of simple digraphs.SUB-RSCC function is formed by two operators of rough set theory (RST),k-step upper approximation set and k-step R-related,which are the main contributors to time consumption.Then the invocation times of SUB-RSCC decide the efficiency of GRSCC algorithm.Based on the SCCs correlations among vertices,GRSCC algorithm introduces granulation strategy to reduce the invocation times of SUB-RSCC function,then improve the mining efficiency.Two new SCCs correlations are found by analysis of SCCs in the framework of RST,then a new vertex granulation strategy is designed to granulate the vertex set of target digraphs.In order to reduce the invocation times of SUB-RSCC function to a greater extent,a method called k-step search of vertex granule is proposed.Finally,combining with GRSCC algorithm,an algorithm called KGRSCC for mining SCCs based on k-step search of vertex granule and RST is proposed.Experimental results show that,compared with RSCC,GRSCC and Tarjan algorithms,the proposed KGRSCC algorithm has better performance.

Key words: k-step search of vertex granule, Granulation strategy, Graph theory, Rough set, Strongly connected components

中图分类号: 

  • TP181
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