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。
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.
作者
程富豪
徐泰华
陈建军
宋晶晶
杨习贝
CHENG Fu-hao;XU Tai-hua;CHEN Jian-jun;SONG Jing-jing;YANG Xi-bei(School of Computer,Jiangsu University of Science and Technology,Zhenjiang,Jiangsu 212000,China;Key Laboratory of Data Science and Intelligent Application,Fujian Province University,Zhangzhou,Fujian 363000,China)
出处
《计算机科学》
CSCD
北大核心
2022年第8期97-107,共11页
Computer Science
基金
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)。
关键词
强连通分量
粗糙集
图论
粒化策略
顶点粒k步搜索
Strongly connected components
Rough set
Graph theory
Granulation strategy
k-step search of vertex granule
作者简介
通讯作者:徐泰华(xth19890410@163.com);程富豪:(cfh_vip@163.com)。
