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Connectivity Index of Generalized Uncertain Graph.
Wang, Jinchan; Gao, Xiulian; Zhou, Xiaoshuang; Guo, Changyou; Yin, Xiuling.
Affiliation
  • Wang J; School of Mathematics and Big Data, Dezhou University, Dezhou 253023, China.
  • Gao X; School of Mathematics and Big Data, Dezhou University, Dezhou 253023, China.
  • Zhou X; School of Mathematics and Big Data, Dezhou University, Dezhou 253023, China.
  • Guo C; School of Information Management, Dezhou University, Dezhou 253023, China.
  • Yin X; School of Mathematics and Big Data, Dezhou University, Dezhou 253023, China.
Comput Intell Neurosci ; 2022: 4571530, 2022.
Article in En | MEDLINE | ID: mdl-35655500
In the application of classical graph theory, there always are various indeterministic factors. This study studies the indeterministic factors in the connected graph by employing the uncertainty theory. First, this study puts forward two concepts: generalized uncertain graph and its connectivity index. Second, it presents a new algorithm to compute the connectivity index of an uncertain graph and generalized uncertain graph and verify this algorithm with typical examples. In addition, it proposes the definition and algorithm of α-connectivity index of generalized uncertain graph and verifies the stability and efficiency of this new algorithm by employing numerical experiments.

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Comput Intell Neurosci Journal subject: INFORMATICA MEDICA / NEUROLOGIA Year: 2022 Document type: Article Affiliation country: Country of publication:

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Comput Intell Neurosci Journal subject: INFORMATICA MEDICA / NEUROLOGIA Year: 2022 Document type: Article Affiliation country: Country of publication: