New Specifications for Exponential Random Graph Models

By Tom A. B. Snijders, P. Pattison, Garry Robins, Mark S. Handcock. 2006

1,512 citations recorded by the source corpus.

Subfields: Geometry and Topology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/000abae9bfe85dc0b6a58046d41c6c5838cc1a97

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In Statistical and Nonlinear Physics

Citations in the corpus

The statistical physics of real-world networks (2018, methodology): “The mathematical framework is that of exponential random graphs (ERG), which has been first introduced in the social sciences and statistics [17–25] as a convenient formulation relying on numerical techniques such as Markov chain Monte Carlo algorithms.”
Inference in Curved Exponential Family Models for Networks (2006, methodology): “However, it is well known that these simple MCMC schemes often fail for various reasons to produce well-mixed chains (Snijders 2002; Handcock 2002, 2003; Snijders et al. 2004).”
A guide to null models for animal social network analysis (2017, methodology): “ERGMs are used to generate hypotheses about what structural processes underpin the formation of social networks (Snijders et al. 2006), and function by randomly adding and removing edges to see how they change the network.”
Respondent-Driven Sampling: An Assessment of Current Methodology (2009, methodology): “These features were represented in the ERGM by choosing network statistics to represent the mean degree, the relative activity levels of the two groups, and homophily (based on using the \infected" status as a dyadic covariate).”
Sensitivity of MRQAP Tests to Collinearity and Autocorrelation Conditions (2007, methodology): “For binary and other coarsely grained discrete data, however, linear models are less appropriate and other models for spatial or network data, such as those proposed by Snijders et al. (2006) or Heagerty and Lele (1998), may be used.”
Exponential Random Graph Models for Social Networks (2014, background): “Hypothèse de Bernoulli : la probabilité d’observer un lien dépend de la densité du graphe Hypothèse de dépendance dyadique : le lien entre A et B est dépendant du lien entre B et A Hypothèse de dépendance de Markov : basée sur les travaux de Besag (1974), elle revient à considérer que deux liens son”
Manual for RSiena (2011, background): “69 (Snijders et al., 2006), corresponding to an internal effect parameter of 69,”
MODELING SOCIAL NETWORKS FROM SAMPLED DATA. (2010, methodology): “These are the number of edges (essentially the density) and the geometrically weighted edgewise shared partner statistic (denoted by GWESP), a measure of the transitivity structure in the network [Snijders et al. (2006)].”
Culture, Cognition, and Collaborative Networks in Organizations (2011, methodology): “…guidelines for fitting ERGMs, see Morris, Handcock, and Hunter 2008; O’Malley and Marsden 2008; Robins et al. 2007; Robins, Pattison, and Wang 2009; Snijders et al. 2006).13 (See Part B in the online supplement for further background about ERGMs and details of the procedure we followed to…”
Understanding network formation in strategy research: Exponential random graph models (2016, methodology): “A number of subsequent advances, including adaptations for local “neighborhood” network structures (Pattison and Robins, 2002) and methods for overcoming the problem of model degeneracy (Hunter and Handcock, 2006; Robins et al., 2009; Snijders et al., 2006) have helped develop ERGMs into a viable to”
Achieving social-ecological fit through bottom-up collaborative governance: an empirical investigation (2015, background): “Thus, although collaborative governance is often thought of as a managed process (e.g., Sørensen and Torfing 2006), it largely relies on collaborating actors actively seeking to engage in collaborative activities to collectively solve problems (Lubell 2013, Lubell et al. 2014).”
Efficient Subgraph Matching by Postponing Cartesian Products (2016, background): “Subgraph matching has a wide range of applications including protein interaction network analysis [13], social network analysis [17], and chemical compound search [20].”
An introduction to exponential random graph (p*) models for social networks (2007, methodology): “It is for such reasons that Snijders et al (2006) introduced their new specifications for exponential random graph models, mentioned in section 4.6, and discussed in other papers in this special edition.”
Estimating and understanding exponential random graph models (2011, background): “Snijders [52] and Handcock [31] demonstrate this empirically with further discussion in [53].”