Inference in Curved Exponential Family Models for Networks

By David R. Hunter, Mark S. Handcock. 2006

554 citations recorded by the source corpus.

Subfields: Geometry and Topology, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3af458fd0147c4d939f83d9c74d0bf1d1789ba43

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Citations in the corpus

Birds of a feather, or friend of a friend? using exponential random graph models to investigate adolescent social networks* (2009, methodology): “To capture this phenomenon, Hunter (2007) and Hunter and Handcock (2006) described a statistic called the geometrically weighted edgewise shared partner distribution (GWESP), a reparameterization of a statistic of Snijders et al. (2006).”
Exponential random graph models for multilevel networks (2013, methodology): “…from 5 The -values for alternating-k-stars described here are equally applicable to he geometrically weighted degree distribution as described in Hunter and Handcock 2006) which uses a ratio parameter ̨ = ln ( ). ltiple (alternating-)star parameters. the simulated graph distribution along with…”
The analysis of social networks (2008, background): “…and k2 [ 1 again governs the rate at which the regression coefficients of k-star terms 16 An equivalent statistic based on the degree distribution itself is known as the ‘‘geometrically weighted degree’’ statistic; see Hunter and Handcock (2006). decline as the order of k-triangles rises.”
Recent developments in exponential random graph (p*) models for social networks (2007, methodology): “…than having many simulation runs within the program, as is the case for SIENA and pnet, it implements very few simulation runs (possibly only one) but then weights the graphs within the simulations in ways that optimizes estimation (see Handcock, 2003b; Hunter and Handcock, 2006, for details).”
MODELING SOCIAL NETWORKS FROM SAMPLED DATA. (2010, methodology): “Section 4 presents the rich Exponential Family Random Graph Model (ERGM) family of models that has been applied to complete network data.”
Goodness of Fit of Social Network Models (2008, methodology): “Much recent effort has been focused on inference for social network models (e.g., Holland and Leinhardt 1981; Strauss and Ikeda 1990; Snijders 2002; Hunter and Handcock 2006), but comparatively little work has tested the goodness of ﬁt of the models.”
How Do They ‘End Up Together’? A Social Network Analysis of Self-Control, Homophily, and Adolescent Relationships (2011, methodology): “Triad closure is parameterized using the geometrically weighted edgewise shared partner distribution (Hunter 2007 and Hunter and Handcock 2006 ):”
Specification of Exponential-Family Random Graph Models: Terms and Computational Aspects. (2008, methodology): “The optional argument fixed indicates whether the scale parameter lambda is to be ﬁt as a curved exponential-family model (see Hunter and Handcock 2006).”
4. A Relational Event Framework for Social Action (2008, background): “…within the social sciences, particularly when dealing with phenomena such as social networks (e.g., see Snijders [1996]; Robins et al. [2001]; Wasserman and Robins [2005]; Hunter and Handcock [2006]) or location systems (Butts 2007a) in which system components are nearly always interactive.”
statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data. (2008, methodology): “The ergm package provides a ﬂexible framework for representing such processes with “curved exponential family models” (Hunter and Handcock 2006).”
Curved exponential family models for social networks (2007, methodology): “This entire procedure is described in detail in Hunter and Handcock (2006).”
Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory (2010, background): “The combination of these in total seven motifs led to the first exponential random graph model that was unfortunately degenerate [71].”
An introduction to exponential random graph (p*) models for social networks (2007, methodology): “Although there are variations between different Monte Carlo estimation techniques (Snijders, 2002; Hunter and Handcock, 2006 ), they are based on the same central approach: simulation of a distribution of random graphs from a starting set of parameter values, and subsequent refinement of the parame”
A Survey of Statistical Network Models (2009, methodology): “Handcock et al. [137] approach this stochastic blockmodeling problem through a combination of latent space models and traditional clustering.”