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A new algorithm to find fuzzy Hamilton cycle in a fuzzy network using adjacency matrix and minimum vertex degree

Overview of attention for article published in SpringerPlus, October 2016
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Title
A new algorithm to find fuzzy Hamilton cycle in a fuzzy network using adjacency matrix and minimum vertex degree
Published in
SpringerPlus, October 2016
DOI 10.1186/s40064-016-3473-x
Pubmed ID
Authors

A. Nagoor Gani, S. R. Latha

Abstract

A Hamiltonian cycle in a graph is a cycle that visits each node/vertex exactly once. A graph containing a Hamiltonian cycle is called a Hamiltonian graph. There have been several researches to find the number of Hamiltonian cycles of a Hamilton graph. As the number of vertices and edges grow, it becomes very difficult to keep track of all the different ways through which the vertices are connected. Hence, analysis of large graphs can be efficiently done with the assistance of a computer system that interprets graphs as matrices. And, of course, a good and well written algorithm will expedite the analysis even faster. The most convenient way to quickly test whether there is an edge between two vertices is to represent graphs using adjacent matrices. In this paper, a new algorithm is proposed to find fuzzy Hamiltonian cycle using adjacency matrix and the degree of the vertices of a fuzzy graph. A fuzzy graph structure is also modeled to illustrate the proposed algorithms with the selected air network of Indigo airlines.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 13%
Other 1 13%
Student > Doctoral Student 1 13%
Student > Bachelor 1 13%
Professor 1 13%
Other 1 13%
Unknown 2 25%
Readers by discipline Count As %
Mathematics 3 38%
Unspecified 1 13%
Agricultural and Biological Sciences 1 13%
Sports and Recreations 1 13%
Unknown 2 25%