#BlockModel
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paperposterbot.bsky.social
arXiv📈🤖
Identifying Hierarchical Structures in Network Data
By
arXiv:2410.02929v1 Announce Type: new Abstract: In this paper, we introduce a hierarchical extension of the stochastic blockmodel to identify multilevel community structures in networks. We also present a Markov chain Monte Carlo (MCMC) and a variational Bayes algorithm to fit the model and obtain approximate posterior inference. Through simulated and real datasets, we demonstrate that the model successfully identifies communities and supercommunities when they exist in the data. Additionally, we observe that the model returns a single supercommunity when there is no evidence of multilevel community structure. As expected in the case of the single-level stochastic blockmodel, we observe that the MCMC algorithm consistently outperforms its variational Bayes counterpart. Therefore, we recommend using MCMC whenever the network size allows for computational feasibility.
September 22, 2026 at 7:36 PM
arxivecongnbot.bsky.social
arXiv📈🤖
Occam Factor for Random Graphs: Erd\"{o}s-R\'{e}nyi, Independent Edge, and Rank-1 Stochastic Blockmodel
By
arXiv:2305.06465v4 Announce Type: replace Abstract: We investigate the evidence/flexibility (i.e., "Occam") paradigm and demonstrate the theoretical and empirical consistency of Bayesian evidence for the task of determining an appropriate generative model for network data. This model selection framework involves determining a collection of candidate models, equipping each of these models' parameters with prior distributions derived via the encompassing priors method, and computing or approximating each models' evidence. We demonstrate how such a criterion may be used to select the most suitable model among the Erd\"{o}s-R\'{e}nyi (ER) model, independent edge (IE) model, and rank-1 stochastic blockmodel (SBM). The Erd\"{o}s-R\'{e}nyi may be considered as being linearly nested within IE, a fact which permits exponential family results. The rank-1 SBM is not so ideal, so we propose a numerical method to approximate its evidence. We apply this paradigm to brain connectome data. Future work necessitates deriving and equipping additional candidate random graph models with appropriate priors so they may be included in the paradigm.
September 22, 2026 at 12:36 AM
soc-net-bot.bsky.social
Directed mixed membership stochastic blockmodel
https://arxiv.org/abs/2101.02307
Mixed membership modeling for undirected networks has been extensively explored in network science over the past few years. Despite the substantial progress made for undirected cases, handling mixed membership st...📈🤖
September 9, 2026 at 7:08 AM
cranupdates.bsky.social
New on CRAN: nethist (1.0.0). View at https://CRAN.R-project.org/package=nethist
nethist: Network Histograms
Estimates network histograms, a blockmodel approximation to the graphon underlying a network's connectivity pattern, for both single-layer and multilayer networks. Implements graphon estimation methods including the profile-likelihood method of Olhede and Wolfe (2014) &lt;<a href="https://doi.org/10.1073%2Fpnas.1400374111" target="_top">doi:10.1073/pnas.1400374111</a>&gt; and the least-squares method of Gao, Lu, and Zhou (2015) &lt;<a href="https://doi.org/10.1214%2F15-AOS1354" target="_top">doi:10.1214/15-AOS1354</a>&gt; for single-layer networks, and the multilayer extension of Song and Olhede (2026) &lt;<a href="https://doi.org/10.48550%2FarXiv.2608.14536" target="_top">doi:10.48550/arXiv.2608.14536</a>&gt;.
CRAN.R-project.org
September 2, 2026 at 1:17 PM
cranberriesfeed.bsky.social
New CRAN package nethist with initial version 1.0.0
#rstats
https://cran.r-project.org/package=nethist
CRAN: Package nethist
Estimates network histograms, a blockmodel approximation to the graphon underlying a network's connectivity pattern, for both single-layer and multilayer networks. Implements graphon estimation methods including the profile-likelihood method of Olhede and Wolfe (2014) &lt;<a href="https://doi.org/10.1073%2Fpnas.1400374111" target="_top">doi:10.1073/pnas.1400374111</a>&gt; and the least-squares method of Gao, Lu, and Zhou (2015) &lt;<a href="https://doi.org/10.1214%2F15-AOS1354" target="_top">doi:10.1214/15-AOS1354</a>&gt; for single-layer networks, and the multilayer extension of Song and Olhede (2026) &lt;<a href="https://doi.org/10.48550%2FarXiv.2608.14536" target="_top">doi:10.48550/arXiv.2608.14536</a>&gt;.
cran.r-project.org
September 2, 2026 at 12:02 PM
paperposterbot.bsky.social
arXiv📈🤖
Joint Estimation of Sparse Multilayer Networks via Graph Limits
By Song, Olhede
Network datasets in modern applications often involve multiple types of interactions occurring over a shared set of individuals. Characterizing the generating mechanisms of these interactions can be enhanced by joint modelling, as shared vertices allow layers to help explain the structure of other layers. We model multiplex observations using graph limits, called a scaled set of graphons, and develop a nonparametric joint estimator based on blockmodel approximations, termed the multi-network histogram. This nonparametric framework captures each layer's varying sparsity and connection structure, accounting for heterogeneity via shared latent variables across all layers. We establish the theoretical properties of the multi-network histogram, providing an upper bound for the weighted mean integrated squared error and deriving the optimal bandwidth that minimizes this error. By leveraging information across layers, this joint modelling achieves a reduction in error and a smaller optimal bandwidth, which enables high-resolution estimation even in sparser layers. Its usefulness is demonstrated through simulation studies and an application to socioeconomic networks in an Indian village.
August 17, 2026 at 5:29 PM
paperposterbot.bsky.social
arXiv📈🤖
Spectral clustering of network time series via the sample covariance matrix
By Martin, Agterberg, Cucuringu et al
Spectral clustering for community detection is analysed in multivariate time series models whose dependence structure is determined by an unobserved stochastic blockmodel. We establish that spectral clustering of the sample covariance matrix achieves exact recovery of the underlying communities. The recovery rates depend explicitly on the network size, sample length, block separation, and degree of data dependence. This demonstrates that exact community recovery under a stochastic blockmodel is possible even when the adjacency matrix is unobserved. Our theory provides extensions of both classical and fine-grained matrix perturbation theory to the setting of dependent data, which may be of independent interest.
August 5, 2026 at 5:37 AM
paperposterbot.bsky.social
arXiv📈🤖
Latent community paths in VAR-type models via dynamic directed spectral co-clustering
By Kim, Baek
This paper proposes a dynamic network framework for uncovering latent community paths in high-dimensional VAR-type models. By embedding a degree-corrected stochastic co-blockmodel (ScBM) into the transition matrices of VAR-type systems, we separate sending and receiving roles at the node level and summarize complex directional dependence in an interpretable low-dimensional form. Our method integrates directed spectral co-clustering with eigenvector smoothing to track how directional groups split, merge, or persist over time. This framework accommodates both periodic VAR (PVAR) models for cyclical seasonal evolution and generalized VHAR models for structural transitions across ordered dependence horizons. We establish non-asymptotic misclassification bounds for both procedures and provide supporting evidence through Monte Carlo experiments. Applications to U.S.\ nonfarm payrolls distinguish a recurrent business-centered core from more mobile, seasonally sensitive sectors. In global stock volatilities, the results reveal a compact U.S.-centered long-horizon block, a Europe-heavy developed core, and a more dynamic short-horizon reallocation of peripheral and bridge markets.
April 15, 2026 at 4:47 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Uncovering Functional Blocks in Interregional Production Networks: Evidence from Input-Output Linkages in Japan
By Fujishima
This paper examines the latent functional block structure of Japan's production network using interregional input-output data. To isolate non-trivial production linkages, we first estimate a structural gravity model to account for spatial frictions and economic scale, and then apply a weighted stochastic blockmodel (SBM) to the resulting residual network. Because these residual linkages often connect distant regions, the SBM is well suited to grouping region-industry pairs based on their shared macroeconomic roles. The results reveal that even after explicitly filtering out the mechanical effects of geographic proximity, the network is organized into functional blocks that maintain a high degree of regional coherence. Beyond this baseline spatial clustering, we find evidence of cross-regional integration, a structural bifurcation between manufacturing and urban services in metropolitan areas, and broadly spanning primary sectors. These findings provide a network-based perspective on regional coordination, offering guidance for how structurally defined production blocks-rather than simple geographic proximity-can inform wide-area policy design.
March 27, 2026 at 4:41 PM
paperposterbot.bsky.social
link 📈🤖
Non-asymptotic goodness-of-fit tests and model selection in valued stochastic blockmodels (Almendra-Hern\'andez, Bakenhus, Karwa et al) A valued stochastic blockmodel (SBM) is a general way to view networked data in which nodes are grouped into blocks and links between them are measured b
October 16, 2025 at 4:41 PM
soc-net-bot.bsky.social
Non-asymptotic goodness-of-fit tests and model selection in valued stochastic blockmodels
https://arxiv.org/abs/2510.13636
A valued stochastic blockmodel (SBM) is a general way to view networked data in which nodes are grouped into blocks and links between them are measured by counts or label...📈🤖
October 16, 2025 at 4:33 PM
paperposterbot.bsky.social
link 📈🤖
Model-based clustering in simple hypergraphs through a stochastic blockmodel () arXiv:2210.05983v3 Announce Type: replace
Abstract: We propose a model to address the overlooked problem of node clustering in simple hypergraphs. Simple hypergraphs are suitable when a node may not appear mu
July 5, 2025 at 7:05 PM
mathst-bot.bsky.social
asymptotic regime where MFVI `works' for the celebrated latent Dirichlet allocation (LDA) model. Focusing on the mixed membership stochastic blockmodel (MMSB), we show that the vanilla fully factorized MFVI, often used in the literature, is [3/4 of https://arxiv.org/abs/2506.01893v1]
June 3, 2025 at 6:09 AM
paperposterbot.bsky.social
link 📈🤖
A Unified Framework for Community Detection and Model Selection in Blockmodels (Bhadra, Tang, Sengupta) Blockmodels are a foundational tool for modeling community structure in networks, with the stochastic blockmodel (SBM), degree-corrected blockmodel (DCBM), and popularity-adjusted block
May 29, 2025 at 4:40 PM
statme-bot.bsky.social
Building on recent theoretical insights about the spectral geometry of these models, we propose a unified framework for simultaneous community detection and model selection across the full blockmodel hierarchy. A key innovation is the use of loss [3/6 of https://arxiv.org/abs/2505.22459v1]
May 29, 2025 at 6:19 AM
statme-bot.bsky.social
arXiv:2505.22459v1 Announce Type: new
Abstract: Blockmodels are a foundational tool for modeling community structure in networks, with the stochastic blockmodel (SBM), degree-corrected blockmodel (DCBM), and popularity-adjusted blockmodel (PABM) [1/6 of https://arxiv.org/abs/2505.22459v1]
May 29, 2025 at 6:19 AM
statme-bot.bsky.social
hierarchical multiplex stochastic blockmodel (HMPSBM), that 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 [2/6 of https://arxiv.org/abs/2505.03441v1]
May 7, 2025 at 6:19 AM
shufflerror.kalkattak.de
Something is starting. Blockmodel and some practice for simplified topology.

#whan #graffiti #blender #3D
3D cgi graffiti letters. rose colored on a grey green floor. lightning is faded from top center to unlit background.
April 20, 2025 at 6:55 PM
statme-bot.bsky.social
remaining node is assigned to a community based on these estimates. We prove that predictive assignment achieves strong consistency under the stochastic blockmodel and its degree-corrected version. We also demonstrate the empirical performance of [5/6 of https://arxiv.org/abs/2503.16730v1]
March 24, 2025 at 6:18 AM
statme-bot.bsky.social
easy to implement, computationally fast, and feature a data-driven approach for tuning parameter selection.
We establish theoretical guarantees for both methods under the Multilayer Stochastic Blockmodel with Covariates (MSBM-C), demonstrating their [3/6 of https://arxiv.org/abs/2503.09156v1]
March 13, 2025 at 6:18 AM
statme-bot.bsky.social
blockmodel. In this work, we formulate a framework for testing for repeated motif hierarchical structure in the stochastic blockmodel framework. We describe a model which naturally expresses networks as a hierarchy of [5/6 of https://arxiv.org/abs/2503.01024v1]
March 4, 2025 at 6:24 AM
lizsgoode.bsky.social
New paper out, co-authored with Prof Alasdair Blair. Read our reflection on an academic conference: the 2024 #IBILTA #blockmodel conference, hosted by Victoria University in Mebourne, Australia. Online now in the Journal of Block and Intensive Learning & Teaching: ibilta.vu.edu.au/index.php/jb...
Back to Block: Re-imagining student learning in a hyper-connected world through an international academic conference | Journal of Block and Intensive Learning and Teaching
This article reflects on the International Block and Intensive Teaching Association (IBILTA) conference held in Melbourne, Australia, in July 2024. Perspectives are offered from two delegates who joined the conference from differing institutional and national contexts. One is a Teaching Scholar from Southern Cross University, Australia, and the other an Associate Pro Vice-Chancellor from De Montfort University, UK. We reflect on the value of attending the conference in person, some of the challenges, and offer thoughts on ‘where to next’.
ibilta.vu.edu.au
December 16, 2024 at 8:25 AM
lizsgoode.bsky.social
Pleased to see a new paper online on #assessment policy reform, and its impact, at a #highered institution.

Led by Prof Erica Wilson and co-authored with Prof Thomas Roche.

#highereducation #higheredpolicy #blockmodel

url.au.m.mimecastprotect.com/s/lnZ9Cr81m2...
Transforming assessment policy: improving student outcomes through an immersive block model
This paper describes how one Australian university achieved deep assessment reform through transition to the Southern Cross Model: a 6-week, immersive block learning approach. There are few studies...
url.au.m.mimecastprotect.com
December 6, 2024 at 6:43 PM
herdjournal.bsky.social
Designing learning to suit students. #BlockModel futurecampus.com.au/2024/11/19/d...
Designing learning to suit students - Future Campus
Southern Cross U adopted the block teaching model in 2020, to address lower than sector average success and retention. So...
futurecampus.com.au
December 3, 2024 at 8:53 PM
herdjournal.bsky.social
Supporting the academic success of underrecognised higher education students through an immersive block model

T Roche, E Wilson, E Goode & J W McKenzie

🔓→ doi.org/10.1080/0729...

#HigherEd #BlockModel #StudentSuccess #UnderrepresentedStudents #MinorityStudents #ActiveLearning #ImmersiveLearning
Figure 1. Teaching terms in the Southern Cross Model.
November 17, 2024 at 3:40 AM