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Many real-world networks represent dynamic systems with interactions thatchange over time, often in uncoordinated ways and at irregular intervals. Forexample, university students connect in intermittent groups that repeatedlyform and dissolve based on multiple factors, including their lectures,interests, and friends. Such dynamic systems can be represented as multilayernetworks where each layer represents a snapshot of the temporal network. Inthis representation, it is crucial that the links between layers accuratelycapture real dependencies between those layers. Often, however, thesedependencies are unknown. Therefore, current methods connect layers based onsimplistic assumptions that do not capture node-level layer dependencies. Forexample, connecting every node to itself in other layers with the same weightcan wipe out dependencies between intermittent groups, making it difficult oreven impossible to identify them. In this paper, we present a principledapproach to estimating node-level layer dependencies based on the networkstructure within each layer. We implement our node-level coupling method in thecommunity detection framework Infomap and demonstrate its performance comparedto current methods on synthetic and real temporal networks. We show that ourapproach more effectively constrains information inside multilayer communitiesso that Infomap can better recover planted groups in multilayer benchmarknetworks that represent multiple modes with different groups and betteridentify intermittent communities in real temporal contact networks. Theseresults suggest that node-level layer coupling can improve the modeling ofinformation spreading in temporal networks and better capture intermittentcommunity structure.

Constrained information flows in temporal networks reveal intermittent communities