Abstract
There are frequently many kinds of relationships in various real networks, which can be represented by multi-relationships complex network. It is a fundamental function for detecting the community structure for identifying the edges between structures in complex networks. Previous community structure detection algorithms often limited by the limitation of network topology structure arising from one relationship, but our algorithm based on a semi-supervised clustering algorithm and multi-subnet composited complex network can overcome the restriction. Through experiment analysis, our algorithm is compared to classical spectral clustering algorithm and nonnegative matrix factorization (NMF) by using artificial generated datasets. The Experimental results show that community structures which are divided by our proposed algorithm are more obscure.
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 callDisclaimer: 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.
