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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Veröffentlicht in:Mathematics (Basel) Jg. 12; H. 11; S. 1723
Hauptverfasser: He, Huixing, Xu, Taihua, Chen, Jianjun, Cui, Yun, Song, Jingjing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.06.2024
Schlagworte:
Algorithms
Apexes
Computing time
Efficiency
Granulation
granulation strategy
Graph theory
Machine learning
Mathematical research
parallel
Parallel processing
SCCs correlations
strongly connected components
China
ISSN:2227-7390, 2227-7390
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:2227-7390
2227-7390
DOI:10.3390/math12111723