A Granulation Strategy-Based Algorithm for Computing Strongly Connected Components in Parallel
Granular computing (GrC) is a methodology for reducing the complexity of problem solving and includes two basic aspects: granulation and granular-based computing. Strongly connected components (SCCs) are a significant subgraph structure in digraphs. In this paper, two new granulation strategies were...
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| Vydáno v: | Mathematics (Basel) Ročník 12; číslo 11; s. 1723 |
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01.06.2024
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| Abstract | Granular computing (GrC) is a methodology for reducing the complexity of problem solving and includes two basic aspects: granulation and granular-based computing. Strongly connected components (SCCs) are a significant subgraph structure in digraphs. In this paper, two new granulation strategies were devised to improve the efficiency of computing SCCs. Firstly, four SCC correlations between the vertices were found, which can be divided into two classes. Secondly, two granulation strategies were designed based on correlations between two classes of SCCs. Thirdly, according to the characteristics of the granulation results, the parallelization of computing SCCs was realized. Finally, a parallel algorithm based on granulation strategy for computing SCCs of simple digraphs named GPSCC was proposed. Experimental results show that GPSCC performs with higher computational efficiency than algorithms. |
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| AbstractList | Granular computing (GrC) is a methodology for reducing the complexity of problem solving and includes two basic aspects: granulation and granular-based computing. Strongly connected components (SCCs) are a significant subgraph structure in digraphs. In this paper, two new granulation strategies were devised to improve the efficiency of computing SCCs. Firstly, four SCC correlations between the vertices were found, which can be divided into two classes. Secondly, two granulation strategies were designed based on correlations between two classes of SCCs. Thirdly, according to the characteristics of the granulation results, the parallelization of computing SCCs was realized. Finally, a parallel algorithm based on granulation strategy for computing SCCs of simple digraphs named GPSCC was proposed. Experimental results show that GPSCC performs with higher computational efficiency than algorithms. |
| Audience | Academic |
| Author | He, Huixing Xu, Taihua Chen, Jianjun Cui, Yun Song, Jingjing |
| Author_xml | – sequence: 1 givenname: Huixing surname: He fullname: He, Huixing – sequence: 2 givenname: Taihua surname: Xu fullname: Xu, Taihua – sequence: 3 givenname: Jianjun surname: Chen fullname: Chen, Jianjun – sequence: 4 givenname: Yun surname: Cui fullname: Cui, Yun – sequence: 5 givenname: Jingjing surname: Song fullname: Song, Jingjing |
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| Cites_doi | 10.1016/j.ijar.2019.12.001 10.1109/TKDE.2021.3138994 10.1137/20M1312149 10.1109/TNNLS.2023.3300916 10.1016/j.ins.2024.120403 10.1016/j.knosys.2023.110821 10.1145/3543542 10.1016/j.asoc.2023.110458 10.1007/BF00121128 10.1007/s10462-022-10177-6 10.1109/ACCESS.2021.3077409 10.1016/j.ins.2016.01.065 10.1007/978-1-84800-998-1 10.1109/TKDE.2023.3237833 10.1109/TFUZZ.2022.3173684 10.1109/TCYB.2021.3054593 10.1109/FOCS46700.2020.00108 10.1007/978-3-540-70952-7 10.1007/3-540-45591-4 10.1109/TCYB.2020.2964011 10.1016/j.eswa.2021.116390 10.3390/e24081154 10.1109/TCYB.2023.3257274 10.1137/0201010 10.1016/j.knosys.2019.105301 10.1145/2851141.2851161 10.1016/j.knosys.2018.06.009 10.1016/j.ijar.2018.09.005 10.1007/s41066-019-00204-3 10.1016/j.ejor.2019.08.030 10.1007/s10489-023-05123-0 10.1016/j.knosys.2018.02.038 10.1109/TFUZZ.2023.3287834 10.3934/mbe.2024116 10.1007/s00453-018-0452-3 10.1016/j.parco.2017.11.001 10.1093/logcom/exp003 |
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| SubjectTerms | Algorithms Apexes Computing time Efficiency Granulation granulation strategy Graph theory Machine learning Mathematical research parallel Parallel processing SCCs correlations strongly connected components |
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