Decomposing Large Networks: An Approach Based on the MCA based Community Detection

The emergence of the big data has called for considering new methodologiesto analyze big networks. In these particular contexts there are many casesin which it is important to take into account not only the single node but groupsof nodes which can have the same or similar functions on a defined network. Onlarge networks it is important to represent them in a meaningful way. Interval dataseems an adequate representation which can be used to represent these networks.The specific contribution of this work it is to show the way in which is possible torank the different structural characteristics of the different robust communities representedby the network. The rank applied to the structural characteristics allows theunderstanding also of the relevant core of the network