Disentangling group and link persistence in Dynamic Stochastic Block models

We present a new way of finding communities in networks that change over time.

Submitted to Journal of Statistical Physics (2017)

P. Barucca, F. Lillo, P. Mazzarisi, D. Tantari

LQ placeholderWe present a new way of finding communities in networks that change over time.

We study the inference of a model of temporal networks in which both communities and links keep memory of previous network state. By considering maximum likelihood inference from single snapshot observation of the network, we show that link persistence decreases the detectability threshold, preventing the inference of communities even when they are in principle strong enough, while community persistence tends to restore this possibility. Then we show that the inferred communities share a maximum overlap with those of a specific previous instant of time, corresponding to the maximum of a time-lagged assortativity parameter, and therefore they can be closer to those of the past than of the present. We analytically characterize these transitions as a function of the memory parameters of the model.

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Taming complexity

M. Reeves, S. Levin, T. Fink, A. Levina

Harvard Business Review

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Degree-correlations in a bursting dynamic network model

F. Vanni, P. Barucca

Journal of Economic Interaction and Coordination

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Scale of non-locality for a system of n particles

S. Talaganis, I. Teimouri

Sub. to Physical Review D

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How much can we influence the rate of innovation?

T. Fink, M. Reeves

Science Advances

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