Neutrosophic theory has many applications in graph theory, interval valued neutrosophic graph (IVNG) is the generalization of fuzzy graph, intuitionistic fuzzy graph and single valued neutrosophic graph. In this paper, we introduced some types of IVNGs, which are subdivision IVNGs, middle IVNGs, total IVNGs and interval valued neutrosophic line graphs (IVNLGs), also discussed the isomorphism, c...

In the present paper, we introduce an extension of the conceptual graph model suitable to the representation 7 of data which are modelized using fuzzy sets. We extend the specialization relation of the conceptual graph model to fuzzy conceptual graphs. Lastly we introduce a new way of comparing conceptual graphs, using the 9 idea that a graph may be compatible with another graph with a given de...

In this paper, 2−bondage set of a fuzzy graph G is defined. The 2−bondage number, b2(G) is the minimum cardinality among all 2−bondage sets of G. The condition for a 2−bondage set of a fuzzy graph to be a bondage set is also given. The exact values of b2(G) is determined for several classes of fuzzy graphs.

In this paper, the concept of a fuzzy graph structure is introduced based on the concept of graph structure given by E. Sampathkumar, in Bull. Kerala Math. Assoc., Vol 3, No.2 (2006 December), 65123. New concepts like ρi − edge, ρi − path, ρi − cycle, ρi − tree, ρi − forest, fuzzy ρi − cycle, fuzzy ρi − tree, fuzzy ρi − forest etc. are introduced. Some results are obtained. We continue the stud...

Let D be the minimum dominating set of intuitionistic fuzzy graph G. The minimum intuitionistic fuzzy cardinality of all edge dominating set of intuitionistic fuzzy graph G is known as edge domination number and it is denoted by γe(G). In this Paper, we initiate some definitions onedge dominating set concerning intuitionistic fuzzy sets. Further, we investigate some results onedge domination nu...

The purpose of this paper is to develop a new two-stage method for fuzzy multi-objective linear program and apply to engineering project portfolio selection. In the fuzzy multi-objective linear program, all the objective coefficients, technological coefficients and resources are trapezoidal fuzzy numbers (TrFNs). An order relationship for TrFNs is introduced by using the interval expectation of...

Graph-based semi-supervised learning has recently emerged as a promising approach to data-sparse learning problems in natural language processing. They rely on graphs that jointly represent each data point. The problem of how to best formulate the graph representation remains an open research topic. In this paper, we introduce a type-2 fuzzy arithmetic to characterize the edge weights of a form...

The depletion of natural resources in the last century now threatens our planet and the life of future generations. For the sake of sustainable development, this paper pioneers an interesting and practical problem of dense substructure (i.e., maximal cliques) mining in a fuzzy graph where the edges are weighted by the degree of membership. For parameter 0 ≤ λ ≤ 1 (also called fuzzy cut in fuzzy...