Flow stability for dynamic community detection.

The novel framework for community detection in temporal networks I have been working on with Jean-Charles Delvenne and Renaud Lambiotte is out on arXiv.

In this work, we derive a method based on a dynamical process evolving on the temporal network and restricted by its activation pattern that allows to consider the full temporal information of the system. Our method allows dynamics that do not necessarily reach a steady state, or follow a sequence of stationary states. We show that the temporal evolution of networks leads to potentially asymmetrical relations between vertices that can be captured by using two network partitions fora given time interval: the forward partition and the backward partition. We also show that our method allows one to reveal different dynamical scales present in temporal networks by varying the rate of the dynamical process. Our framework encompasses several well-known heuristics as special cases.

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