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We present a reformulation of modularity that allows the analysis of thecommunity structure in networks of correlated data. The new modularitypreserves the probabilistic semantics of the original definition even when thenetwork is directed, weighted, signed, and has self-loops. This is the mostgeneral condition one can find in the study of any network, in particular thosedefined from correlated data. We apply our results to a real network ofcorrelated data between stores in the city of Lyon (France).

Analysis of community structure in networks of correlated data