Regular Decomposition of Large Graphs: Foundation of a Sampling Approach to Stochastic Block Model Fitting
Sign Up to like & getrecommendations! 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
Complex networks of material flow in manufacturing and logistics: Modeling, analysis, and prediction using stochastic block models
Sign Up to like & getrecommendations! 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
Sign Up to like & getrecommendations! 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
Corrected Bayesian Information Criterion for Stochastic Block Models
Sign Up to like & getrecommendations! 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
Link prediction in complex networks via modularity-based belief propagation*
Sign Up to like & getrecommendations! 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.
Sign Up to like & getrecommendations! 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
Large deviations of semisupervised learning in the stochastic block model.
Sign Up to like & getrecommendations! 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.
Sign Up to like & getrecommendations! 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
Sign Up to like & getrecommendations! 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
Strong Consistency of Spectral Clustering for Stochastic Block Models
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Information Theory"
DOI: 10.1109/tit.2019.2934157
Abstract: In this paper we prove the strong consistency of several methods based on the spectral clustering techniques that are widely used to study the community detection problem in stochastic block models (SBMs). We show that… read more here.
Keywords: spectral clustering; block; strong consistency; block models ... See more keywords
Estimation of the Number of Communities in the Stochastic Block Model
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Information Theory"
DOI: 10.1109/tit.2020.3016331
Abstract: In this article we introduce an estimator for the number of communities in the Stochastic Block Model (SBM), based on the maximization of a penalized version of the so-called Krichevsky-Trofimov mixture distribution. We prove its… read more here.
Keywords: communities stochastic; number; block model; estimation number ... See more keywords