Solving maximum fuzzy clique problem with neural n

Authors: Malay Bhattacharyya Sanghamitra Bandyopadhyay

Publish Date: 2009/10/01

Volume: 1, Issue: 4, Pages: 281-

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Abstract

The maximum clique problem is an important problem in graph theory Many reallife problems are still being mapped into this problem for their effective solutions A natural extension of this problem that has emerged very recently in many reallife networks is its fuzzification The problem of finding the maximum fuzzy clique has been formalized on fuzzy graphs and subsequently addressed in this paper It has been shown here that the problem reduces to an unconstrained quadratic 0–1 programming problem Using a maximum neural network along with mutation capability of genetic adaptive systems the reduced problem has been solved Empirical studies have been done by applying the method on stock flow graphs to identify the collusion set which contains a group of traders performing unfair trading among themselves Additionally it has been applied on a gene coexpression network to find out significant gene modules and on some benchmark graphs

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