title: Temporal stability of network partitions.
abstract: We present a method to find the best temporal partition at any time scale and rank the relevance of partitions found at different time scales. This method is based on random walkers coevolving with the network and as such constitutes a generalization of partition stability to the case of temporal networks. We show that, when applied to a toy model and real data sets, temporal stability uncovers structures that are persistent over meaningful time scales as well as important isolated events, making it an effective tool to study both abrupt changes and gradual evolution of a network mesoscopic structures.
authors.affiliation: ISI Foundation, Via Alassio 11/c, 10126 Turin, Italy.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Petri
authors.affiliation: Centre for Neuroimaging Sciences, Institute of Psychiatry, De Crespigny Park, King's College London, London SE5 8AF, United Kingdom.
authors.forename: Paul
authors.initials: P
authors.lastname: Expert
mesh_terms: D002648:Child; D004467:Economics; D006801:Humans; D008962:Models, Theoretical; D012574:Schools; D013995:Time