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Community detection is a common task in the analysis of network data but most current community detection algorithms provide either only a single partition or a very large number of plausible ones, neither of which gives an interpretable summary of the possible structures. Here the authors provide a solution to this problem, in the form of an algorithm based on the minimum description length principle that identifies minimal sets of archetypal, highly representative partitions of a network that succinctly summarize the plausible community structures.