Abstract
Network partitioning, an unsupervised method of learning helps in discovering the ‘communities’ that reside in a network and share common properties of interest. We have partitioned a network generated from an ensemble of granular materials by using a highly accurate and resolution limit free community detection model proposed by Ronhovde and Nussinov (RN model). We associate the best communities with naturally identifiable structures in the ensemble to find out the ‘best’ resolution for partitioning the network. The Shannon entropy distribution of individual communities at different resolutions indicates existence of hierarchical structure in the network which is non-obvious and otherwise non-observable. Effect of disorder (irregular regions present in the granular ensemble) on the community evolution in the invariant network and its stability is analysed.
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.
