Simultaneous global and local clustering in multiplex networks with covariate information

Corneck, Joshua; Cohen, Edward A. K.; Martin, James S.; Patel, Lekha; Shuler, Kurtis Woodfin; Sanna Passino, Francesco

doi:10.1093/comnet/cnaf049

Simultaneous global and local clustering in multiplex networks with covariate information

Journal Article · Mon Jan 26 00:00:00 EST 2026 · Journal of Complex Networks (Online)

DOI:https://doi.org/10.1093/comnet/cnaf049· OSTI ID:3020955

^[1]; Cohen, Edward A. K. ^[1]; Martin, James S. ^[1]; ^[2]; Shuler, Kurtis Woodfin ^[2]; Sanna Passino, Francesco ^[1]

Imperial College, London (United Kingdom)
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Understanding both global and layer-specific group structures is useful for uncovering complex patterns in networks with multiple interaction types. In this work, we introduce a new model, the hierarchical multiplex stochastic blockmodel, which simultaneously detects communities within individual layers of a multiplex network while inferring a global node clustering across the layers. A stochastic blockmodel is assumed in each layer, with probabilities of layer-level group memberships determined by a node’s global group assignment. Our model uses a Bayesian framework, employing a probit stick-breaking process to construct node-specific mixing proportions over a set of shared Griffiths–Engen–McCloseky distributions. These proportions determine layer-level community assignment, allowing for an unknown and varying number of groups across layers, while incorporating nodal covariate information to inform the global clustering. We propose a scalable variational inference procedure with parallelisable updates for application to large networks. Extensive simulation studies demonstrate our model’s ability to accurately recover both global and layer-level clusters in complicated settings, and applications to real data showcase the model’s effectiveness in uncovering interesting latent network structure.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003525

OSTI ID:: 3020955

Report Number(s):: SAND--2026-17862J; 1777057

Journal Information:: Journal of Complex Networks (Online), Journal Name: Journal of Complex Networks (Online) Journal Issue: 1 Vol. 14; ISSN 2051-1329

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United States

Language:: English

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