Detectability of Macroscopic Structures in Directed Networks: a Stochastic Block Model Approach

Disentangling network macroscopic structures is one of the funding problems in complexity science. One of the most basic models of communities in networks are the stochastic block models. It was recently shown that in this case the detectability of real communities only from the network topology is limited. Even though the results were shown only for planted partition, where there are only two parameters, the conclusions are universal.

We examined a more general case of directed stochastic block model. More interestingly, we shown that by introducing a dissymmetry of direction, we are able to increase the range of the detectable phase. Importantly, this qualitative change holds for an entire class of hardly detectable models, where both the average in- and out-degree are the same across all groups.

Additionally, we found an analytical approximation of the phase transition points, which were confirmed by extensive numerical simulations.

Authors: 
Mateusz Wilinski, Daniele Tantari, Piero Mazzarisi and Fabrizio Lillo
Room: 
1
Date: 
Monday, September 24, 2018 - 11:15 to 11:30

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