Computer Science ›› 2022, Vol. 49 ›› Issue (8): 97-107.doi: 10.11896/jsjkx.210700202

• Database & Big Data & Data Science • Previous Articles     Next Articles

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).

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

CLC Number: 

  • TP181
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