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. In weighted multilayer noisy networks , a natural hierarchy is induced by the assumption of all but one block being under the `umbrella' of signal , while a single block is classified as exhibiting the same characteristics as interstitial interactions and subsumed into ambient noise. We use hierarchical variational inference to jointly detect block-structures and differentiate the communities' local signals from global noise. We propose a novel algorithm to find clusters in Gaussian weighted multilayer networks with a focus on these principles called Stochastic Block (with) Ambient Noise Model (SBANM)  . 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 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.
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In weighted multilayer noisy networks , a natural hierarchy is induced by the assumption of
We induce a hierarchy of classifications in weighted multilayer networks by assuming that
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block being under the `umbrella' of signal
cluster (block) are governed by unique local signals
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exhibiting the same characteristics as interstitial interactions and subsumed into ambient noise. We use hierarchical variational inference
ambient noise, which behaves identically as interactions across differing blocks. Hierarchical variational inference is employed
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block-structures and differentiate the communities' local signals from
and typologize block-structures as local signals or
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We propose a novel algorithm to find clusters in Gaussian weighted multilayer networks with a focus on these principles
These principles are incorporated into novel community detection algorithm
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for multilayer weighted networks