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Many evolving complex systems can be modeled via dynamic networks. Animportant problem in dynamic network research is community detection, whichidentifies groups of topologically related nodes. Typically, this problem isapproached by assuming either that each time point has a distinct communityorganization or that all time points share one community organization. Inreality, the truth likely lies between these two extremes, since some timeperiods can have community organization that evolves while others can havecommunity organization that stays the same. To find the compromise, we considercommunity detection in the context of the problem of segment detection, whichidentifies contiguous time periods with consistent network structure.Consequently, we formulate a combined problem of segment community detection(SCD), which simultaneously partitions the network into contiguous timesegments with consistent community organization and finds this communityorganization for each segment. To solve SCD, we introduce SCOUT, anoptimization framework that explicitly considers both segmentation quality andpartition quality. SCOUT addresses limitations of existing methods that can beadapted to solve SCD, which typically consider only one of segmentation qualityor partition quality. In a thorough evaluation, SCOUT outperforms the existingmethods in terms of both accuracy and computational complexity.

SCOUT: simultaneous time segmentation and community detection in dynamic networks