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Snijders Scientific stochastic agent-based modelling framework
Stochastic Agent Based Modelling Framework, supplied by Snijders Scientific, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/agent-based+modeling+framework+and+model/stochastic+block+model/pmc04486283-62-5-10
Average 90 stars, based on 1 article reviews
stochastic agent-based modelling framework - by Bioz Stars, 2026-10
90/100 stars

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Article Title: A Deep Dynamic Latent Block Model for Co-clustering of Zero-Inflated Data Matrices
Article Snippet: .. In particular, for the Stochastic Block Model (SBM, Nowicki and Snijders 2001) than for LBM, although SBM is a special case of LBM, not needing that data matrices are square and/or symmetrical. ..

Article Title: Clustering by deep latent position model with graph convolutional network
Article Snippet: .. On the one hand, the stochastic block model [SBM, (Wang and Wong 1987; Nowicki and Snijders 2001)] is widely used to detect communities or more general clusters of nodes (Lee and Wilkinson 2019). ..

Article Title: Deep dynamic co-clustering of count data streams: application to pharmacovigilance
Article Snippet: .. Notably, more work has been done in dynamic network clustering, particularly with the Stochastic Block Model (SBM) (Nowicki and Snijders 2001), which is a special case of the latent block model (LBM) adapted for non-square and non-symmetric data matrices. ..

Article Title: Encoder embedding for general graph and node classification
Article Snippet: .. Examples include: • Euclidean Distance: • Inner Product: • Normalized Inner Product: • Radial basis function kernel: • Random Dot Product Graph (Young and Scheinerman 2007; Athreya et al. 2018): • Stochastic Block Model (Holland et al. 1983; Snijders and Nowicki 1997): Let B = [B(k , l)] ∈ [0, 1]K×K be a block probability matrix. ..

Article Title: Community influence analysis in social networks
Article Snippet: Computational Statistics and Data Analysis 202 (2025) 108037 Contents lists available at ScienceDirect Computational Statistics and Data Analysis journal homepage: www.elsevier.com/locate/csda Community influence analysis in social networks Yuanxing Chen a, Kuangnan Fang b, Wei Lan c,∗, Chih-Ling Tsai d, Qingzhao Zhang b,e,∗∗ a Yau Mathematical Sciences Center, Tsinghua University, Beijing, China b Department of Statistics and Data Science, Xiamen University, Xiamen, China c School of Statistics and Center of Statistical Research, Southwestern University of Finance and Economics, Chengdu, China d Graduate School of Management, University of California, Davis, United States of America e The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, China A R T I C L E I N F O A B S T R A C T Keywords: Fused lasso Influence power Nodal heterogeneity Quasi-maximum likelihood estimator Subgroup analysis Heterogeneous influence detection across network nodes is an important task in network analysis.. A community influence model (CIM) is proposed to allow nodes to be classified into different communities (i.e., clusters or groups) such that the nodes within the same community share the common influence parameter.. Employing the quasi-maximum likelihood approach, together with the fused lasso-type penalty, both the number of communities and the influence parameters can be estimated without imposing any specific distribution assumption on the error terms.

other:

Article Title: Onset of a conceptual outline map to get a hold on the jungle of cluster analysis
Article Snippet: We illustrate by means of the relation between the latent stochastic blockmodel (Nowicki & Snijders, 2001) and the mixed membership stochastic blockmodel (Airoldi et al., 2008).

Article Title: Automated detection of edge clusters via an overfitted mixture prior
Article Snippet: Some notable model-based approaches for static networks are the latent position cluster model (Handcock et al., 2007), stochastic block model (SBM) (Nowicki & Snijders, 2001), degree-corrected SBM (Karrer & Newman, 2011), and mixed-membership SBM (Airoldi et al., 2008).

Article Title: Stochastic Block Smooth Graphon Model
Article Snippet: The most popular ones of this type are the Stochastic Blockmodel (Holland, Laskey, and Leinhardt 1983; Snijders and Nowicki 1997 and 2001) as well as its variants (Airoldi et al. 2008; Karrer and Newman 2011), the Latent Distance Model (Hoff and coauthors, 2002, 2007, 2009, 2021; Ma, Ma, and Yuan 2020), and the Graphon Model (Lovász and coauthors, 2006, 2007, 2010; Diaconis and Janson 2007).

Generated:

Article Title: Community influence analysis in social networks
Article Snippet: Computational Statistics and Data Analysis 202 (2025) 108037 Contents lists available at ScienceDirect Computational Statistics and Data Analysis journal homepage: www.elsevier.com/locate/csda Community influence analysis in social networks Yuanxing Chen a, Kuangnan Fang b, Wei Lan c,∗, Chih-Ling Tsai d, Qingzhao Zhang b,e,∗∗ a Yau Mathematical Sciences Center, Tsinghua University, Beijing, China b Department of Statistics and Data Science, Xiamen University, Xiamen, China c School of Statistics and Center of Statistical Research, Southwestern University of Finance and Economics, Chengdu, China d Graduate School of Management, University of California, Davis, United States of America e The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, China A R T I C L E I N F O A B S T R A C T Keywords: Fused lasso Influence power Nodal heterogeneity Quasi-maximum likelihood estimator Subgroup analysis Heterogeneous influence detection across network nodes is an important task in network analysis.. A community influence model (CIM) is proposed to allow nodes to be classified into different communities (i.e., clusters or groups) such that the nodes within the same community share the common influence parameter.. Employing the quasi-maximum likelihood approach, together with the fused lasso-type penalty, both the number of communities and the influence parameters can be estimated without imposing any specific distribution assumption on the error terms.



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