Community detection in stochastic block models via penalized variational estimation
Sign Up to like & getrecommendations! 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
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
Partially Exchangeable Stochastic Block Models for (Node-Colored) Multilayer Networks
Sign Up to like & getrecommendations! 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
Tensorial and bipartite block models for link prediction in layered networks and temporal networks
Sign Up to like & getrecommendations! Published in 2019 at "Physical review. E"
DOI: 10.1103/physreve.99.032307
Abstract: Many real-world complex systems are well represented as multilayer networks; predicting interactions in those systems is one of the most pressing problems in predictive network science. To address this challenge, we introduce two stochastic block… read more here.
Keywords: temporal networks; block models; tensorial bipartite; bipartite block ... 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
Efficient Inference in Stochastic Block Models With Vertex Labels
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Network Science and Engineering"
DOI: 10.1109/tnse.2019.2913949
Abstract: We study the stochastic block model with two communities where vertices contain side information in the form of a vertex label. These vertex labels may have arbitrary label distributions, depending on the community memberships. We… read more here.
Keywords: vertex labels; inference stochastic; efficient inference; block models ... See more keywords
Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models.
Sign Up to like & getrecommendations! Published in 2019 at "F1000Research"
DOI: 10.12688/f1000research.18705.1
Abstract: Background: Biological entities such as genes, promoters, mRNA, metabolites or proteins do not act alone, but in concert in their network context. Modules, i.e., groups of nodes with similar topological properties in these networks characterize… read more here.
Keywords: block models; network; biomolecular networks; analysis ... See more keywords