Abstract
Community structure is an important property of networks. Being able to identify communities can provide invaluable help in exploiting and understanding both social and non-social networks. Several algorithms have been developed up till now. However all these algorithms can work well only with small or moderate networks with vertexes of order 10/sup 4/. Additionally, all existing algorithms are off-line and can not work well with highly dynamic networks such as Web, in which Web pages are updated frequently every day. When the already clustered network is updated, the entire network including original and incremental parts has to be recalculated, even only slight changes involved. To address this problem we develop an incremental algorithm which allows for detecting community structure in large-scale and dynamic networks. Based on the community structure it detect previously our algorithm takes little time to reclassify the entire network including both original and incremental parts. Furthermore, our algorithm is faster than most existing algorithms such as Girvan and Newman's algorithm and its improved versions. Also our algorithm can help to visualize these community structures in network and provide a new approach to research the evolving process of dynamic networks.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
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.