Recent developments in exponential random graph (p*) models for social networks

By Garry Robins, Tom A. B. Snijders, Peng Wang, Mark S. Handcock, P. Pattison. Soc. Networks, 2007

864 citations recorded by the source corpus.

Subfields: Statistical and Nonlinear Physics, Transportation

View Paper: https://www.semanticscholar.org/paper/00350a2b4adbaba0293ec10f73b759cfddde166e

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Citations in the corpus

A Survey of Statistical Network Models (2009, methodology): “[256] describe problems associated with the estimation of parameters in many ERGMs, involving near degeneracies of the likelihood function and thus of methods used to estimate parameters using maximum likelihood.”
The Economic Consequences of Social Network Structure (2017, background): “There is a powerful and natural formulation of such models known as “exponential random graph models” that incorporate such interdependencies (ERGMs or p∗ models - see Robins et al (2007); Jackson (2008a) for background).”
Closure, connectivity and degree distributions: Exponential random graph (p*) models for directed social networks (2009, methodology): “Snijders et al. (2006) and Robins et al. (2007) provided interpreations of the associated parameter: a positive estimate is evidence hat the network contains a skewed degree distribution with some igher degree nodes, whereas a negative parameter suggests that 108 G. Robins et al. / Social Networks…”
Beyond and Below Racial Homophily: ERG Models of a Friendship Network Documented on Facebook1 (2010, methodology): “The positive GWESP coefficient and negative GWOD coefficient together indicate that there is a tendency toward clustering in our network that is caused by scattered groups of overlapping triangles rather than by overlapping clusters of particularly sociable students (see Robins et al. 2007b).”
A statnet Tutorial. (2008, neutral): “This is the GWESP (geometrically weighted edgewise shared partner) statistic; For more information on it, and the equivalent \alternating k-triangle" statistic, see Snijders et al. (2006); Robins, Snijders, Wang, Handcock, and Pattison (2007b) or Hunter (2007).”
Statistical Models for Social Networks (2011, background): “The Exponential Random Graph Models with these sufficient statistics are further discussed in Snijders et al. (2006); Hunter (2007); Robins et al.”
Nonparametric Latent Feature Models for Link Prediction (2009, methodology): “This rules out approaches like the popular p∗ model that uses global quantities of the graph, such as how many edges or triangles are present [2, 3].”
Networks and the Epidemiology of Infectious Disease (2010, methodology): “etwork structures even if the complete network is unknown (Handcock and Jones, 2004; Robins et al., 2004). Due to their simplicity, ERGMs are widely used by statisticians and social network analysts (Robins et al., 2007). Despite signiﬁcant advances in recent years (e.g. Go odreau (2007)), ERGMs sti”
Consistency of community detection in networks under degree-corrected stochastic block models (2011, background): “The applications are many and varied, including social networks [31, 37], gene regulatory networks [33], recommender systems and security monitoring.”
A Sequential Importance Sampling Algorithm for Generating Random Graphs with Prescribed Degrees (2011, neutral): “99 , Frank and Strauss 86 , Newman 03 , Robins et al.”
Exponential random graph models for multilevel networks (2013, methodology): “We can apply the current ERGM specifications for one-mode networks (Snijders et al., 2006; Robins et al., 2007b, 2009) to look at within level network structures, given other effects in the model. zQ (x) are the network statistics for structural effects within the bipartite affiliation network.”
ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks. (2008, methodology): “by Sampson (1968) after analyzing the trends in the pattern of ties over time, are indicated by the three di↵erent shapes and colors of nodes.”