Characterization of 2-Path Product Signed Graphs with Its Properties
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-06
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
A signed graph is a simple graph where each edge receives a sign positive or negative.
Such graphs are mainly used in social sciences where individuals represent vertices friendly relation between them as a positive edge and enmity as a negative edge.
In signed graphs, we define these relationships (edges) as of friendship (“+” edge) or hostility (“-” edge).
A 2-path product signed graph S#^S of a signed graph S is defined as follows: the vertex set is the same as S and two vertices are adjacent if and only if there exists a path of length two between them in S.
The sign of an edge is the product of marks of vertices in S where the mark of vertex u in S is the product of signs of all edges incident to the vertex.
In this paper, we give a characterization of 2-path product signed graphs.
Also, some other properties such as sign-compatibility and canonically-sign-compatibility of 2-path product signed graphs are discussed along with isomorphism and switching equivalence of this signed graph with 2-path signed graph.
American Psychological Association (APA)
Sinha, Deepa& Sharma, Deepakshi. 2017. Characterization of 2-Path Product Signed Graphs with Its Properties. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1139833
Modern Language Association (MLA)
Sinha, Deepa& Sharma, Deepakshi. Characterization of 2-Path Product Signed Graphs with Its Properties. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1139833
American Medical Association (AMA)
Sinha, Deepa& Sharma, Deepakshi. Characterization of 2-Path Product Signed Graphs with Its Properties. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1139833
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139833