Abstraction of meso-scale network architecture in granular ensembles using ‘big data analytics’ tools

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.