Pattern Recognition Letters
An algorithm to compute the supremum of max-min powers and a property of fuzzy graphs
Pattern Recognition Letters
An optimal algorithm to find the degrees of connectedness in an undirected edge-weighted graph
Pattern Recognition Letters
Pattern Recognition Letters
Information Sciences—Intelligent Systems: An International Journal
Cycles and cocycles of fuzzy graphs
Information Sciences: an International Journal
Domination in fuzzy graphs - I
Pattern Recognition Letters
A characterization of fuzzy trees
Information Sciences: an International Journal
Information Sciences: an International Journal
Fuzzy end nodes in fuzzy graphs
Information Sciences: an International Journal
Information Sciences: an International Journal
Toward a generalized theory of uncertainty (GTU): an outline
Information Sciences—Informatics and Computer Science: An International Journal
Is there a need for fuzzy logic?
Information Sciences: an International Journal
Node connectivity and arc connectivity of a fuzzy graph
Information Sciences: an International Journal
A nonparametric classification method based on K-associated graphs
Information Sciences: an International Journal
Information Sciences: an International Journal
A characterization of partial blocks in weighted graphs
Information Processing Letters
Menger's theorem for fuzzy graphs
Information Sciences: an International Journal
Fuzzy graph modeling for text segmentation from land map images
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Cycle connectivity in fuzzy graphs
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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The concept of connectivity plays an important role in both theory and applications of fuzzy graphs. Depending on the strength of an arc, this paper classifies arcs of a fuzzy graph into three types namely @a-strong, @b-strong and @d-arcs. The advantage of this type of classification is that it helps in understanding the basic structure of a fuzzy graph completely. We analyze the relation between strong paths and strongest paths in a fuzzy graph and obtain characterizations for fuzzy bridges, fuzzy trees and fuzzy cycles using the concept of @a-strong, @b-strong and @d-arcs. An arc of a fuzzy tree is @a-strong if and only if it is an arc of its unique maximum spanning tree. Also we identify different types of arcs in complete fuzzy graphs.