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   Articles with "stochastic block" as a keyword



Regular Decomposition of Large Graphs: Foundation of a Sampling Approach to Stochastic Block Model Fitting

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recommendations! Published in 2019 at "Data Science and Engineering"

DOI: 10.1007/s41019-019-0084-x

Abstract: We analyze the performance of regular decomposition, a method for compression of large and dense graphs. This method is inspired by Szemerédi’s regularity lemma (SRL), a generic structural result of large and dense graphs. In… read more here.

Keywords: graphs; method; model; stochastic block ... See more keywords
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Complex networks of material flow in manufacturing and logistics: Modeling, analysis, and prediction using stochastic block models

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recommendations! Published in 2020 at "Journal of Manufacturing Systems"

DOI: 10.1016/j.jmsy.2020.06.015

Abstract: Abstract Modeling complex systems as networks of interacting elements has gained increased attention in recent years. So far, network modeling in manufacturing and logistics has often focused on the description of system properties. In the… read more here.

Keywords: network; material flow; manufacturing logistics; stochastic block ... See more keywords

On equivalence of likelihood maximization of stochastic block model and constrained nonnegative matrix factorization

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recommendations! Published in 2018 at "Physica A: Statistical Mechanics and its Applications"

DOI: 10.1016/j.physa.2018.02.068

Abstract: Community structures detection in complex network is important for understanding not only the topological structures of the network, but also the functions of it. Stochastic block model and nonnegative matrix factorization are two widely used… read more here.

Keywords: model; nonnegative matrix; matrix factorization; stochastic block ... See more keywords

Community detection in stochastic block models via penalized variational estimation

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recommendations! Published in 2024 at "Journal of Statistical Computation and Simulation"

DOI: 10.1080/00949655.2024.2439481

Abstract: Stochastic Block Models (SBMs) have emerged as a powerful framework for modelling community structures in networks. However, accurately determining the number of communities in SBMs remains challenging. In this paper, we propose a novel approach… read more here.

Keywords: stochastic block; block models; estimation; community detection ... See more keywords

Corrected Bayesian Information Criterion for Stochastic Block Models

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recommendations! Published in 2019 at "Journal of the American Statistical Association"

DOI: 10.1080/01621459.2019.1637744

Abstract: Abstract Estimating the number of communities is one of the fundamental problems in community detection. We re-examine the Bayesian paradigm for stochastic block models (SBMs) and propose a “corrected Bayesian information criterion” (CBIC), to determine… read more here.

Keywords: corrected bayesian; criterion; block models; bayesian information ... See more keywords

Partially Exchangeable Stochastic Block Models for (Node-Colored) Multilayer Networks

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recommendations! Published in 2024 at "Journal of the American Statistical Association"

DOI: 10.1080/01621459.2025.2507825

Abstract: Abstract Multilayer networks generalize single-layered connectivity data in several directions. These generalizations include, among others, settings where multiple types of edges are observed among the same set of nodes (edge-colored networks) or where a single… read more here.

Keywords: multilayer networks; node colored; stochastic block; colored multilayer ... See more keywords

Link prediction in complex networks via modularity-based belief propagation*

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recommendations! Published in 2017 at "Chinese Physics B"

DOI: 10.1088/1674-1056/26/3/038902

Abstract: Link prediction aims at detecting missing, spurious or evolving links in a network, based on the topological information and/or nodes' attributes of the network. Under the assumption that the likelihood of the existence of a… read more here.

Keywords: network; link; model; stochastic block ... See more keywords

Statistical mechanics of the minimum vertex cover problem in stochastic block models.

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recommendations! Published in 2019 at "Physical review. E"

DOI: 10.1103/physreve.100.062101

Abstract: The minimum vertex cover (Min-VC) problem is a well-known NP-hard problem. Earlier studies illustrate that the problem defined over the Erdös-Rényi random graph with a mean degree c exhibits computational difficulty in searching the Min-VC… read more here.

Keywords: problem; minimum vertex; vertex cover; stochastic block ... See more keywords
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Large deviations of semisupervised learning in the stochastic block model.

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recommendations! Published in 2022 at "Physical review. E"

DOI: 10.1103/physreve.105.034108

Abstract: In semisupervised community detection, the membership of a set of revealed nodes is known in addition to the graph structure and can be leveraged to achieve better inference accuracies. While previous works investigated the case… read more here.

Keywords: block model; deviations semisupervised; large deviations; stochastic block ... See more keywords

Systematic assessment of the quality of fit of the stochastic block model for empirical networks.

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recommendations! Published in 2022 at "Physical review. E"

DOI: 10.1103/physreve.105.054311

Abstract: We perform a systematic analysis of the quality of fit of the stochastic block model (SBM) for 275 empirical networks spanning a wide range of domains and orders of size magnitude. We employ posterior predictive… read more here.

Keywords: quality fit; fit stochastic; block model; stochastic block ... See more keywords

Finite size analysis of the detectability limit of the stochastic block model

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recommendations! Published in 2017 at "Physical Review E"

DOI: 10.1103/physreve.95.062304

Abstract: It has been shown in recent years that the stochastic block model is sometimes undetectable in the sparse limit, i.e., that no algorithm can identify a partition correlated with the partition used to generate an… read more here.

Keywords: finite size; block model; stochastic block; detectability ... See more keywords