Fuzzy graphs

Sunil Mathew, John N. Mordeson, Davender S. Malik

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

Abstract

A graph represents a particular relationship between elements of a set V. It gives an idea about the extent of the relationship between any two elements of V. We can solve this problem by using a weighted graph if proper weights are known. But in most of the situations, the weights may not be known, and the relationships are ‘fuzzy’ in a natural sense. Hence, a fuzzy relation can deal with the situation in a better way. As an example, if V represents certain locations and a network of roads is to be constructed between elements of V, then the costs of construction of the links are fuzzy. But the costs can be compared, to some extent using the terrain and local factors and can be modeled as fuzzy relations. Thus, fuzzy graph models are more helpful and realistic in natural situations.

Original languageEnglish (US)
Title of host publicationStudies in Fuzziness and Soft Computing
PublisherSpringer Verlag
Pages13-83
Number of pages71
Volume363
DOIs
StatePublished - Jan 1 2018

Publication series

NameStudies in Fuzziness and Soft Computing
Volume363
ISSN (Print)1434-9922

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Computational Mathematics

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    Mathew, S., Mordeson, J. N., & Malik, D. S. (2018). Fuzzy graphs. In Studies in Fuzziness and Soft Computing (Vol. 363, pp. 13-83). (Studies in Fuzziness and Soft Computing; Vol. 363). Springer Verlag. https://doi.org/10.1007/978-3-319-71407-3_2