  Many evolving complex systems can be modeled via dynamic networks. An
important problem in dynamic network research is community detection, which
identifies groups of topologically related nodes. Typically, this problem is
approached by assuming either that each time point has a distinct community
organization or that all time points share one community organization. In
reality, the truth likely lies between these two extremes, since some time
periods can have community organization that evolves while others can have
community organization that stays the same. To find the compromise, we consider
community detection in the context of the problem of segment detection, which
identifies 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 time
segments with consistent community organization and finds this community
organization for each segment. To solve SCD, we introduce SCOUT, an
optimization framework that explicitly considers both segmentation quality and
partition quality. SCOUT addresses limitations of existing methods that can be
adapted to solve SCD, which typically consider only one of segmentation quality
or partition quality. In a thorough evaluation, SCOUT outperforms the existing
methods in terms of both accuracy and computational complexity.
