Abstract
It is very important to discovery potential customers or groups for the traditional textile enterprises to enhance its’ market competitiveness. Aiming to discover the implicit characters of the textile-related trade system, A biased estimation of distribution algorithm was proposed to detect the community structure in this paper. This algorithm combined a biased search and a simulated annealing selections strategy which were used to improve both the convergence speed and the accuracy of the EDAs to discovery the communities structure for complex system by maximizing the modularity density. The biased search is efficient by taking into account an asymmetric similarity between any pairs of nodes in network according to the different characteristics and local environment of nodes. The proposed algorithm was applied to detect the community structure for a textile-related trade network with the scale-free character extracted from a set of textile companies by uniquely leveraging each node with economic behavior, and the result show that the algorithm is efficient and competent
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Ambient Intelligence and Humanized Computing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
