Nonmetric MDS consensus community detection

Community detection methods for the analysis of complex networks are increasingly important in modern literature. At the same time it is still an open problem. The approach proposed in this work is to adopt an ensemble procedure for obtaining a consensus matrix from which to perform a nonmetric MDS approach and then a clustering algorithm which allows to get a consensus partition of the nodes. The simulation study offers some interesting insights on the procedure because it shows that it is possible to understand the key nodes and the stable communities by considering different algorithms. The proposed approach is still applied to real data related to a network of patents.