Community Detection in Weighted Multilayer Networks with Ambient Noise
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Abstract (arXiv)
We introduce a novel model for multilayer weighted networks that accounts for global noise in addition to local signals. The model is similar to a multilayer stochastic blockmodel (SBM), but the key difference is that between-block interactions independent across layers are common for the whole system, which we call ambient noise. A single block is also characterized by these fixed ambient parameters to represent members that do not belong anywhere else. This approach allows simultaneous clustering and typologizing of blocks into signal or noise in order to better understand their roles in the overall system, which is not accounted for by existing Blockmodels. We employ a novel application of hierarchical variational inference to jointly detect and differentiate types of blocks. We call this model for multilayer weighted networks the Stochastic Block (with) Ambient Noise Model (SBANM) and develop an associated community detection algorithm. We apply this method to subjects in the Philadelphia Neurodevelopmental Cohort to discover communities of subjects with co-occurrent psychopathologies in relation to psychosis.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 100 references that are in the map. First posted on arXiv 24 Feb 2021.
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30 of 100 references.
References · 30
- 2016Variational Inference: A Review for Statisticianscited by 1,466
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- 2016Community detection in networks: A user guidecited by 105
- 2015Hierarchical Variational Modelscited by 89
- 2019Inference for multiple heterogeneous networks with a common invariant subspacecited by 39
- 2015Statistical clustering of temporal networks through a dynamic stochastic block modelcited by 30
- 2018On a 'Two Truths' Phenomenon in Spectral Graph Clusteringcited by 24
- 2015Modern temporal network theory: A colloquiumcited by 23
- 2014Learning Latent Block Structure in Weighted Networkscited by 22
- 2015Community detection in multi-relational data with restricted multi-layer stochastic blockmodelcited by 14
- 2020Latent space models for multiplex networks with shared structurecited by 14
- 2017Network classification with applications to brain connectomicscited by 12
18 more
- 2017Joint Embedding of Graphscited by 12
- 2015Clustering Network Layers With the Strata Multilayer Stochastic Block Modelcited by 10
- 2017Nonparametric weighted stochastic block modelscited by 10
- 2019Spectral Clustering of Signed Graphs via Matrix Power Meanscited by 10
- 2015Modeling sequences and temporal networks with dynamic community structurescited by 9
- 2014Multilayer stochastic block models reveal the multilayer structure of complex networkscited by 7
- 2019Mutual Information in Community Detection with Covariate Information and Correlated Networkscited by 7
- 2013Weighted Multiplex Networkscited by 4
- 2018Estimating network structure from unreliable measurementscited by 4
- 2013A testing based extraction algorithm for identifying significant communities in networkscited by 3
- 2021Informative core identification in complex networkscited by 3
- 2015Enhanced detectability of community structure in multilayer networks through layer aggregationcited by 2
- 2019Gaussian Mixture Models for Stochastic Block Models with Non-Vanishing Noisecited by 2
- 2015Stochastic Block Models for Multiplex networks: an application to networks of researcherscited by 1
- 2015A testing-based approach to the discovery of differentially correlated variable setscited by 1
- 2019Intertemporal Community Detection in Human Mobility Networkscited by 1
- 2019Role Detection in Bicycle-Sharing Networks Using Multilayer Stochastic Block Modelscited by 1
- 2020Bayesian inference of network structure from unreliable datacited by 1
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