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Extraction of subsets of highly connected nodes ("communities" or modules) is a standard step in the analysis of complex social and biological networks. We here consider the problem of finding a relatively small set of nodes in two labeled weighted graphs that is highly connected in both. While many scoring functions and algorithms tackle the problem, the typically high computational cost of permutation testing required to establish the p -value for the observed pattern presents a major practical obstacle. To address this problem, we here extend the recently proposed CTD ("Connect the Dots") approach to establish information-theoretic upper bounds on the p -values and lower bounds on the size and connectedness of communities that are detectable. This is an innovation on the applicability of CTD, broadening its use to pairs of graphs.

An Information-Theoretic Bound on p-Values for Detecting Communities Shared between Weighted Labeled Graphs