We introduce a novel class of stochastic blockmodel for multilayer weighted networks that accounts for the presence of a global ambient noise that governs between-block interactions. We induce a hierarchy of classifications in weighted multilayer networks by assuming that all but one cluster (block) are governed by unique local signals, while a single block is classified as ambient noise, which behaves identically as interactions across differing blocks  . Hierarchical variational inference is employed to jointly detect and typologize block-structures as local signals or global noise. These principles are incorporated into novel community detection algorithm called Stochastic Block (with) Ambient Noise Model(SBANM) for multilayer weighted networks. We apply this method to several different domains. We focus on the Philadelphia Neurodevelopmental Cohort to discover communities of subjects that form diagnostic categories relating psychopathological symptoms to psychosis.
We introduce a novel class of stochastic blockmodel for multilayer weighted networks that accounts for the presence of a global ambient noise governing between-block interactions. We induce a hierarchy of classifications in weighted multilayer networks by assuming that all but one cluster (block) are governed by unique local signals, while a single block behaves identically as interactions across differing blocks (ambient noise) . Hierarchical variational inference is employed to jointly detect and typologize blocks as signal or noise. We call this model for multilayer weighted networks the Stochastic Block (with) Ambient Noise Model(SBANM) and develop an associated community detection algorithm. Then we apply this method to subjects in the Philadelphia Neurodevelopmental Cohort to discover communities of subjects with similar psychopathological symptoms in relation to psychosis.
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