  A novel approach rooted on the notion of consensus clustering, a strategy
developed for community detection in complex networks, is proposed to cope with
the heterogeneity that characterizes connectivity matrices in health and
disease. The method can be summarized as follows:
  (i) define, for each node, a distance matrix for the set of subjects by
comparing the connectivity pattern of that node in all pairs of subjects; (ii)
cluster the distance matrix for each node; (iii) build the consensus network
from the corresponding partitions; (iv) extract groups of subjects by finding
the communities of the consensus network thus obtained.
  Differently from the previous implementations of consensus clustering, we
thus propose to use the consensus strategy to combine the information arising
from the connectivity patterns of each node. The proposed approach may be seen
either as an exploratory technique or as an unsupervised pre-training step to
help the subsequent construction of a supervised classifier. Applications on a
toy model and two real data sets, show the effectiveness of the proposed
methodology, which represents heterogeneity of a set of subjects in terms of a
weighted network, the consensus matrix.
