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Snijders Scientific random graph models
Random Graph Models, supplied by Snijders Scientific, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/graph+model/graph+models+p+random/pm40820875-33-53-69
Average 86 stars, based on 1 article reviews
random graph models - by Bioz Stars, 2026-11
86/100 stars

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Article Title: Representing Micro-Macro Linkages by Actor-Based Dynamic Network Models
Article Snippet: The currently most used statistical model of this kind is the exponential random graph model ( Wasserman and Pattison, 1996 ; Snijders, Pattison, Robins, and Handcock, 2006 ).

Article Title: Exploring Carbon Mineral Systems: Recent Advances in C Mineral Evolution, Mineral Ecology, and Network Analysis
Article Snippet: Such methods will allow for the systematic study of network features, such as degree distribution, distribution of shared partners, centrality, clustering, connected subgraphs, and cliques, and will employ an exponential random graph model (ERGM) (Frank and Strauss, 1986; Snjiders, 2002; Hunter and Handcock, 2006; Snijders et al., 2006; Hunter, 2007; Pattison et al., 2007; Lusher et al., 2012).

Article Title: Testing the stakeholders' partnership in a tourism waste management network: an ERGM approach.
Article Snippet: The following four model types represent four key milestones in the development of the ERGM: the simple random graph model, dyadic independence model, dyadic dependence model, and high-order dependence model (Snijders et al. 2006; Robins et al. 2007; Stephens et al. 2016).

Article Title: A Monte Carlo Metropolis-Hastings Algorithm for Sampling from Distributions with Intractable Normalizing Constants
Article Snippet: Simulating from distributions with intractable normalizing constants has been a long-standing problem in machine learning.. In this letter, we propose a new algorithm, the Monte Carlo Metropolis-Hastings (MCMH) algorithm, for tackling this problem.. The MCMH algorithm is a Monte Carlo version of the Metropolis-Hastings algorithm.

Article Title: Name order effects in measuring adolescent social networks using rosters
Article Snippet: This method is functionally equivalent to a dyadic-independent exponential random graph model ( Lubbers and Snijders 2007 ; Lusher et al. 2013 , p. 59; Robins et al. 2007a ).



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