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DBLP
Article . 2015
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Bipolar Fuzzy Graphs with Categorical Properties

Authors: Hossein Rashmanlou; Sovan Samanta; Madhumangal Pal; Rajab Ali Borzooei;

Bipolar Fuzzy Graphs with Categorical Properties

Abstract

Theoretical concepts of graphs are highly utilized by computer science applications. Especially in research areas of computer science such as data mining, image segmentation, clustering, image capturing and networking. In this paper, we discussed some properties of the –complement of bipolar fuzzy graphs. Self –complement bipolar fuzzy graphs and self weak –complement bipolar fuzzy graphs are defined and a necessary condition for a bipolar fuzzy graph to be self –complement is given. We defined busy vertices and free vertices in bipolar fuzzy graphs and studied their image under an isomorphism. Categorical properties of bipolar fuzzy graphs are discussed. Also, we investigated some properties of isomorphism on bipolar fuzzy graphs.

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Keywords

Electronic computers. Computer science, Busy vertex and Free vertex, Bipolar fuzzy graphs, µ–complement and self –complement, QA75.5-76.95

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
85
Top 1%
Top 1%
Top 1%
gold