Valued.bib

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@ARTICLE{Bu08n,
  author = {Butts, Carter T.},
  title = {\pkg{network}: A Package for Managing Relational Data in \proglang{R}},
  journal = {Journal of Statistical Software},
  year = {2008},
  volume = {24},
  pages = {1--36},
  number = {2},
  month = may,
  file = {:Bu08n.pdf:PDF},
  issn = {1548-7660},
  url = {http://www.jstatsoft.org/v24/i02}
}

@MANUAL{BuHa13n,
  title = {\pkg{network}: Classes for Relational Data},
  author = {Carter T. Butts and Mark S. Handcock and David R. Hunter},
  address = {Irvine, CA},
  year = {March 15, 2013},
  note = {R package version 1.7.2},
  url = {http://statnet.org/}
}

@ARTICLE{GoHa08s,
  author = {Goodreau, Steven M. and Handcock, Mark S. and Hunter, David R. and
	Butts, Carter T. and Morris, Martina},
  title = {A \pkg{statnet} Tutorial},
  journal = {Journal of Statistical Software},
  year = {2008},
  volume = {24},
  pages = {1--26},
  number = {9},
  month = may,
  accepted = {2007-12-25},
  bibdate = {2007-12-25},
  coden = {JSSOBK},
  day = {25},
  file = {:GoHa08s.pdf:PDF},
  issn = {1548-7660},
  submitted = {2007-06-01},
  url = {http://www.jstatsoft.org/v24/i09}
}

@MANUAL{HaHu13e,
  title = {\pkg{ergm}: Fit, Simulate and Diagnose Exponential-Family Models
	for Networks},
  author = {Mark S. Handcock and David R. Hunter and Carter T. Butts and Steven
	M. Goodreau and Pavel N. Krivitsky and Martina Morris},
  organization = {The Statnet Project (\url{http://www.statnet.org})},
  year = {2013},
  note = {R package version 3.1-0},
  url = {CRAN.R-project.org/package=ergm}
}

@ARTICLE{HaHu08s,
  author = {Handcock, Mark S. and Hunter, David R. and Butts, Carter T. and Goodreau,
	Steven M. and Morris, Martina},
  title = {\pkg{statnet}: Software Tools for the Representation, Visualization,
	Analysis and Simulation of Network Data},
  journal = {Journal of Statistical Software},
  year = {2008},
  volume = {24},
  pages = {1--11},
  number = {1},
  month = may,
  file = {:HaHu08s.pdf:PDF},
  issn = {1548-7660},
  url = {http://www.jstatsoft.org/v24/i01}
}

@ARTICLE{HaRa07m,
  author = {Handcock, Mark S. and Raftery, Adrian E. and Tantrum, Jeremy M.},
  title = {Model-based clustering for social networks},
  journal = {Journal of the Royal Statistical Society: Series A},
  year = {2007},
  volume = {170},
  pages = {301--354},
  number = {2},
  abstract = {Network models are widely used to represent relations between interacting
	units or actors. Network data often exhibit transitivity, meaning
	that two actors that have ties to a third actor are more likely to
	be tied than actors that do not, homophily by attributes of the actors
	or dyads, and clustering. Interest often focuses on finding clusters
	of actors or ties, and the number of groups in the data is typically
	unknown. We propose a new model, the latent position cluster model,
	under which the probability of a tie between two actors depends on
	the distance between them in an unobserved Euclidean 'social space',
	and the actors' locations in the latent social space arise from a
	mixture of distributions, each corresponding to a cluster. We propose
	two estimation methods: a two-stage maximum likelihood method and
	a fully Bayesian method that uses Markov chain Monte Carlo sampling.
	The former is quicker and simpler, but the latter performs better.
	We also propose a Bayesian way of determining the number of clusters
	that are present by using approximate conditional Bayes factors.
	Our model represents transitivity, homophily by attributes and clustering
	simultaneously and does not require the number of clusters to be
	known. The model makes it easy to simulate realistic networks with
	clustering, which are potentially useful as inputs to models of more
	complex systems of which the network is part, such as epidemic models
	of infectious disease. We apply the model to two networks of social
	relations. A free software package in the R statistical language,
	latentnet, is available to analyse data by using the model.},
  file = {:HaRa07m.pdf:PDF},
  publisher = {Blackwell Synergy}
}

@ARTICLE{HuHa08e,
  author = {Hunter, David R. and Handcock, Mark S. and Butts, Carter T. and Goodreau,
	Steven M. and Morris, Martina},
  title = {\pkg{ergm}: A Package to Fit, Simulate and Diagnose Exponential-Family
	Models for Networks},
  journal = {Journal of Statistical Software},
  year = {2008},
  volume = {24},
  pages = {1--29},
  number = {3},
  month = may,
  file = {:HuHa08e.pdf:PDF},
  issn = {1548-7660},
  url = {http://www.jstatsoft.org/v24/i03}
}

@MANUAL{Kr13e,
  title = {\pkg{ergm.count}: Fit, Simulate and Diagnose Exponential-Family Models
	for Networks with Count Edges},
  author = {Pavel N. Krivitsky},
  organization = {The Statnet Project (\url{http://www.statnet.org})},
  year = {2013},
  note = {R package version 3.1-0},
  url = {CRAN.R-project.org/package=ergm.count}
}

@ARTICLE{Kr12e,
  author = {Krivitsky, Pavel N.},
  title = {Exponential-Family Random Graph Models for Valued Networks},
  journal = {Electronic Journal of Statistics},
  year = {2012},
  volume = {6},
  pages = {1100--1128},
  abstract = {Exponential-family random graph models (ERGMs) provide a principled
	and flexible way to model and simulate features common in social
	networks, such as propensities for homophily, mutuality, and friend-of-a-friend
	triad closure, through choice of model terms (sufficient statistics).
	However, those ERGMs modeling the more complex features have, to
	date, been limited to binary data: presence or absence of ties. Thus,
	analysis of valued networks, such as those where counts, measurements,
	or ranks are observed, has necessitated dichotomizing them, losing
	information and introducing biases.
	
	
	In this work, we generalize ERGMs to valued networks. Focusing on
	modeling counts, we formulate an ERGM for networks whose ties are
	counts and discuss issues that arise when moving beyond the binary
	case. We introduce model terms that generalize and model common social
	network features for such data and apply these methods to a network
	dataset whose values are counts of interactions.},
  doi = {10.1214/12-EJS696},
  file = {:Kr12e.pdf:PDF}
}

@MANUAL{KrHa13l,
  title = {\pkg{latentnet}: Latent position and cluster models for statistical
	networks},
  author = {Pavel N. Krivitsky and Mark S. Handcock},
  organization = {The Statnet Project (\url{http://www.statnet.org})},
  year = {2013},
  note = {R package version 2.4-4},
  url = {CRAN.R-project.org/package=latentnet}
}

@ARTICLE{KrHa08f,
  author = {Krivitsky, Pavel N. and Handcock, Mark S.},
  title = {Fitting Position Latent Cluster Models for Social Networks with \pkg{latentnet}},
  journal = {Journal of Statistical Software},
  year = {2008},
  volume = {24},
  pages = {1--23},
  number = {5},
  month = may,
  abstract = {latentnet is a package to fit and evaluate statistical latent position
	and cluster models for networks. Hoff, Raftery, and Handcock (2002)
	suggested an approach to modeling networks based on positing the
	existence of an latent space of characteristics of the actors. Relationships
	form as a function of distances between these characteristics as
	well as functions of observed dyadic level covariates. In latentnet
	social distances are represented in a Euclidean space. It also includes
	a variant of the extension of the latent position model to allow
	for clustering of the positions developed in Handcock, Raftery, and
	Tantrum (2007). The package implements Bayesian inference for the
	models based on an Markov chain Monte Carlo algorithm. It can also
	compute maximum likelihood estimates for the latent position model
	and a two-stage maximum likelihood method for the latent position
	cluster model. For latent position cluster models, the package provides
	a Bayesian way of assessing how many groups there are, and thus whether
	or not there is any clustering (since if the preferred number of
	groups is 1, there is little evidence for clustering). It also estimates
	which cluster each actor belongs to. These estimates are probabilistic,
	and provide the probability of each actor belonging to each cluster.
	It computes four types of point estimates for the coefficients and
	positions: maximum likelihood estimate, posterior mean, posterior
	mode and the estimator which minimizes Kullback-Leibler divergence
	from the posterior. You can assess the goodness-of-fit of the model
	via posterior predictive checks. It has a function to simulate networks
	from a latent position or latent position cluster model.},
  file = {:KrHa08f.pdf:PDF},
  issn = {1548-7660},
  url = {http://www.jstatsoft.org/v24/i05}
}

@ARTICLE{KrHa09r,
  author = {Krivitsky, Pavel N. and Handcock, Mark S. and Raftery, Adrian E.
	and Hoff, Peter D.},
  title = {Representing Degree Distributions, Clustering, and Homophily in Social
	Networks with Latent Cluster Random Effects Models},
  journal = {Social Networks},
  year = {2009},
  volume = {31},
  pages = {204--213},
  number = {3},
  month = jul,
  abstract = {Social network data often involve transitivity, homophily on observed
	attributes, community structure, and heterogeneity of actor degrees.
	We propose a latent cluster random effects model to represent all
	of these features, and we develop Bayesian inference for it. The
	model is applicable to both binary and non-binary network data. We
	illustrate the model using two real datasets: liking between monks
	and coreaderships between Slovenian publications. We also apply it
	to two simulated network datasets with very different network structure
	but the same highly skewed degree sequence generated from a preferential
	attachment process. One has transitivity and community structure
	while the other does not. Models based solely on degree distributions,
	such as scale-free, preferential attachment and power-law models,
	cannot distinguish between these very different situations, but the
	latent cluster random effects model does.},
  doi = {10.1016/j.socnet.2009.04.001},
  file = {:Mine/KrHa09r.pdf:PDF;:KrHa09r.pdf:PDF},
  issn = {0378-8733}
}

@MANUAL{R13r,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2013},
  url = {http://www.R-project.org/}
}

@Article{KrBu12e,
  Title                    = {Exponential-Family Random Graph Models for Rank-Order Relational Data},
  Author                   = {Krivitsky, Pavel N. and Butts, Carter T.},
  Journal                  = {Sociological Methodology},
  Year                     = {2017},
  Volume = {In press},
  Abstract                 = {Rank-order relational data, in which each actor ranks the others according to some criterion, often arise from sociometric measurements of judgment (e.g., self-reported interpersonal interaction) or preference (e.g., relative liking). We propose a class of exponential-family models for rank-order relational data and derive a new class of sufficient statistics for such data, which assume no more than within-subject ordinal properties. Application of MCMC MLE to this family allows us to estimate effects for a variety of plausible mechanisms governing rank structure in cross-sectional context, and to model the evolution of such structures over time. We apply this framework to model the evolution of relative liking judgments in an acquaintance process, and to model recall of relative volume of interpersonal interaction among members of a technology education program.},
  Eprint                   = {http://arxiv.org/abs/1210.0493},
  File                     = {:KrBu12e.pdf:PDF},
  Keywords                 = {ERGM; social networks; ranks; weighted networks; transitivity; mutuality},
  Url                      = {http://arxiv.org/abs/1210.0493}
}


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