references.bib

@Manual{R,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2023},
  url = {https://www.R-project.org/},
}

@Manual{datatable,
  title = {data.table: Extension of `data.frame`},
  author = {Tyson Barrett and Matt Dowle and Arun Srinivasan},
  year = {2023},
  note = {R package version 1.14.10, https://Rdatatable.gitlab.io/data.table,
https://github.com/Rdatatable/data.table},
  url = {https://r-datatable.com},
}

@article{vegayonPowerMulticollinearitySmall2023,
  title = {Power and Multicollinearity in Small Networks: {{A}} Discussion of ``{{Tale}} of {{Two Datasets}}: {{Representativeness}} and {{Generalisability}} of {{Inference}} for {{Samples}} of {{Networks}}'' by {{Krivitsky}}, {{Coletti}} \& {{Hens}}},
  author = {Vega Yon, George G.},
  year = {2023},
  journal = {Journal Of The American Statistical Association}
}

@article{krivitskyTaleTwoDatasets2023a,
  title = {A {{Tale}} of {{Two Datasets}}: {{Representativeness}} and {{Generalisability}} of {{Inference}} for {{Samples}} of {{Networks}}},
  shorttitle = {A {{Tale}} of {{Two Datasets}}},
  author = {Krivitsky, Pavel N. and Coletti, Pietro and Hens, Niel},
  year = {2023},
  journal = {Journal of the American Statistical Association},
  volume = {0},
  number = {0},
  pages = {1--21},
  publisher = {{Taylor \& Francis}},
  issn = {0162-1459},
  doi = {10.1080/01621459.2023.2242627},
  urldate = {2023-12-08},
  abstract = {The last two decades have seen considerable progress in foundational aspects of statistical network analysis, but the path from theory to application is not straightforward. Two large, heterogeneous samples of small networks of within-household contacts in Belgium were collected using two different but complementary sampling designs: one smaller but with all contacts in each household observed, the other larger and more representative but recording contacts of only one person per household. We wish to combine their strengths to learn the social forces that shape household contact formation and facilitate simulation for prediction of disease spread, while generalising to the population of households in the region. To accomplish this, we describe a flexible framework for specifying multi-network models in the exponential family class and identify the requirements for inference and prediction under this framework to be consistent, identifiable, and generalisable, even when data are incomplete; explore how these requirements may be violated in practice; and develop a suite of quantitative and graphical diagnostics for detecting violations and suggesting improvements to candidate models. We report on the effects of network size, geography, and household roles on household contact patterns (activity, heterogeneity in activity, and triadic closure). Supplementary materials for this article are available online.},
  keywords = {ERGM,Exponential-family random graph model,Missing data,Model-based inference,Network size,Regression diagnostics},
  file = {/home/george/Zotero/storage/UJYRYWYL/Krivitsky et al. - 2023 - A Tale of Two Datasets Representativeness and Gen.pdf}
}

@article{miloNetworkMotifsSimple2002a,
  title = {Network {{Motifs}}: {{Simple Building Blocks}} of {{Complex Networks}}},
  author = {Milo, R. and {Shen-Orr}, S. and Itzkovitz, S. and Kashtan, N. and Chklovskii, D. and Alon, U.},
  year = {2002},
  month = oct,
  journal = {Science},
  volume = {298},
  number = {5594},
  pages = {824--827},
  issn = {0036-8075},
  doi = {10.1126/science.298.5594.824},
  pmid = {12399590}
}

@article{krivitskyExponentialfamilyRandomGraph2012,
  title = {Exponential-Family Random Graph Models for Valued Networks},
  author = {Krivitsky, Pavel N.},
  year = {2012},
  journal = {Electronic Journal of Statistics},
  volume = {6},
  pages = {1100--1128},
  issn = {19357524},
  doi = {10.1214/12-EJS696},
  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.},
  keywords = {Conway-Maxwell-Poisson distribution,Count data,Maximum likelihood estimation,P-star model,Transitivity,Weighted network}
}

@article{vegayonExponentialRandomGraph2021,
  title = {Exponential Random Graph Models for Little Networks},
  author = {Vega Yon, George G. and Slaughter, Andrew and de la Haye, Kayla},
  year = {2021},
  journal = {Social Networks},
  volume = {64},
  number = {August 2020},
  pages = {225--238},
  publisher = {{Elsevier}},
  issn = {03788733},
  doi = {10.1016/j.socnet.2020.07.005},
  abstract = {Statistical models for social networks have enabled researchers to study complex social phenomena that give rise to observed patterns of relationships among social actors and to gain a rich understanding of the interdependent nature of social ties and actors. Much of this research has focused on social networks within medium to large social groups. To date, these advances in statistical models for social networks, and in particular, of Exponential-Family Random Graph Models (ERGMS), have rarely been applied to the study of small networks, despite small network data in teams, families, and personal networks being common in many fields. In this paper, we revisit the estimation of ERGMs for small networks and propose using exhaustive enumeration when possible. We developed an R package that implements the estimation of pooled ERGMs for small networks using Maximum Likelihood Estimation (MLE), called ``ergmito''. Based on the results of an extensive simulation study to assess the properties of the MLE estimator, we conclude that there are several benefits of direct MLE estimation compared to approximate methods and that this creates opportunities for valuable methodological innovations that can be applied to modeling social networks with ERGMs.},
  keywords = {Exact statistics,Exponential random graph models,Simulation study,Small networks,Teams}
}

@Manual{ergmito,
  title = {{{ergmito: Exponential Random Graph Models for Small Networks}}},
  author = {George {Vega Yon}},
  note = {R package version 0.3-1},
  publisher = {{CRAN}},
  year={2020},
  url = {https://cran.r-project.org/package=ergmito},
}

@article{stivalaExponentialRandomGraph2020a,
  title = {Exponential Random Graph Model Parameter Estimation for Very Large Directed Networks},
  author = {Stivala, Alex and Robins, Garry and Lomi, Alessandro},
  year = {2020},
  journal = {PLoS ONE},
  volume = {15},
  number = {1},
  pages = {1--23},
  issn = {19326203},
  doi = {10.1371/journal.pone.0227804},
  abstract = {Exponential random graph models (ERGMs) are widely used for modeling social networks observed at one point in time. However the computational difficulty of ERGM parameter estimation has limited the practical application of this class of models to relatively small networks, up to a few thousand nodes at most, with usually only a few hundred nodes or fewer. In the case of undirected networks, snowball sampling can be used to find ERGM parameter estimates of larger networks via network samples, and recently published improvements in ERGM network distribution sampling and ERGM estimation algorithms have allowed ERGM parameter estimates of undirected networks with over one hundred thousand nodes to be made. However the implementations of these algorithms to date have been limited in their scalability, and also restricted to undirected networks. Here we describe an implementation of the recently published Equilibrium Expectation (EE) algorithm for ERGM parameter estimation of large directed networks. We test it on some simulated networks, and demonstrate its application to an online social network with over 1.6 million nodes.},
  pmid = {31978150}
}

@article{krivitskyErgmNewFeatures2023a,
  title = {Ergm 4: {{New Features}} for {{Analyzing Exponential-Family Random Graph Models}}},
  shorttitle = {Ergm 4},
  author = {Krivitsky, Pavel N. and Hunter, David R. and Morris, Martina and Klumb, Chad},
  year = {2023},
  month = jan,
  journal = {Journal of Statistical Software},
  volume = {105},
  pages = {1--44},
  issn = {1548-7660},
  doi = {10.18637/jss.v105.i06},
  urldate = {2023-12-08},
  abstract = {The ergm package supports the statistical analysis and simulation of network data. It anchors the statnet suite of packages for network analysis in R introduced in a special issue in Journal of Statistical Software in 2008. This article provides an overview of the new functionality in the 2021 release of ergm version 4. These include more flexible handling of nodal covariates, term operators that extend and simplify model specification, new models for networks with valued edges, improved handling of constraints on the sample space of networks, and estimation with missing edge data. We also identify the new packages in the statnet suite that extend ergm's functionality to other network data types and structural features and the robust set of online resources that support the statnet development process and applications.},
  copyright = {Copyright (c) 2023 Pavel N. Krivitsky, David R. Hunter, Martina Morris, Chad Klumb},
  langid = {english},
  file = {/home/george/Zotero/storage/WTMLFAGA/Krivitsky et al. - 2023 - ergm 4 New Features for Analyzing Exponential-Fam.pdf}
}


@book{lusherExponentialRandomGraph2013,
  title = {Exponential {{Random Graph Models}} for {{Social Networks}}: {{Theory}}, {{Methods}}, and {{Applications}}},
  shorttitle = {Exponential {{Random Graph Models}} for {{Social Networks}}},
  author = {Lusher, Dean and Koskinen, Johan and Robins, Garry},
  year = {2013},
  publisher = {{Cambridge University Press}},
  abstract = {"Exponential random graph models (ERGMs) are a class of statistical models for social networks. They account for the presence (and absence) of network ties and so provide a model for network structure. An ERGM models a given network in terms of small local tie-based structures, such as reciprocated ties and triangles. A social network can be thought of as being built up of these local patterns of ties, called network configurations xe "network configurations" , which correspond to the parameters in the model. Moreover, these configurations can be considered to arise from local social processes, whereby actors in the network form connections in response to other ties in their social environment. ERGMs are a principled statistical approach to modeling social networks. They are theory-driven in that their use requires the researcher to consider the complex, intersecting and indeed potentially competing theoretical reasons why the social ties in the observed network have arisen. For instance, does a given network structure occur due to processes of homophily xe "actor-relation effects:homophily" , xe "homophily" {\textbackslash}t "see actor-relation effects" reciprocity xe "reciprocity" , transitivity xe "transitivity" , or indeed a combination of these? By including such parameters together in the one model a researcher can test these effects one against the other, and so infer the social processes that have built the network. Being a statistical model, an ERGM permits inferences about whether, in our network of interest, there are significantly more (or fewer) reciprocated ties, or triangles (for instance), than we would expect"--},
  googlebooks = {gyKypohCjDcC},
  isbn = {978-0-521-19356-6},
  langid = {english},
  keywords = {Business \& Economics / Organizational Behavior,FAMILY \& RELATIONSHIPS,Social Science / Research}
}

@article{stivalaExponentialRandomGraph2020a,
  title = {Exponential Random Graph Model Parameter Estimation for Very Large Directed Networks},
  author = {Stivala, Alex and Robins, Garry and Lomi, Alessandro},
  year = {2020},
  journal = {PLoS ONE},
  volume = {15},
  number = {1},
  pages = {1--23},
  issn = {19326203},
  doi = {10.1371/journal.pone.0227804},
  abstract = {Exponential random graph models (ERGMs) are widely used for modeling social networks observed at one point in time. However the computational difficulty of ERGM parameter estimation has limited the practical application of this class of models to relatively small networks, up to a few thousand nodes at most, with usually only a few hundred nodes or fewer. In the case of undirected networks, snowball sampling can be used to find ERGM parameter estimates of larger networks via network samples, and recently published improvements in ERGM network distribution sampling and ERGM estimation algorithms have allowed ERGM parameter estimates of undirected networks with over one hundred thousand nodes to be made. However the implementations of these algorithms to date have been limited in their scalability, and also restricted to undirected networks. Here we describe an implementation of the recently published Equilibrium Expectation (EE) algorithm for ERGM parameter estimation of large directed networks. We test it on some simulated networks, and demonstrate its application to an online social network with over 1.6 million nodes.},
  pmid = {31978150}
}

@book{brooksHandbookMarkovChain2011,
  title = {Handbook of {{Markov Chain Monte Carlo}}},
  author = {Brooks, S. and Gelman, A. and Jones, G. and Meng, X. L.},
  year = {2011},
  publisher = {{CRC Press}},
  isbn = {978-1-4200-7942-5}
}

@article{snijdersNonParametricStandardErrors1999,
  title = {Non-{{Parametric Standard Errors}} and {{Tests}} for {{Network Statistics}}},
  author = {Snijders, Tom A. B. and Borgatti, Stephen P.},
  year = {1999},
  journal = {Connections},
  volume = {22},
  number = {2},
  pages = {1--10},
  abstract = {Two procedures are proposed for calculating standard errors for network statistics. Both are based on resampling of vertices: the first follows the bootstrap approach, the second the jackknife approach. In addition, we demonstrate how to use these estimated standard errors to compare statistics using an approximate t-test and how statistics can also be compared by another bootstrap approach that is not based on approximate normality.}
}

@article{hayeSmokingDiffusionNetworks2019,
  title = {Smoking {{Diffusion}} through {{Networks}} of {{Diverse}}, {{Urban American Adolescents}} over the {{High School Period}}},
  author = {de la Haye, Kayla and Shin, Heesung and {Vega Yon}, George G. and Valente, Thomas W.},
  year = {2019},
  journal = {Journal of Health and Social Behavior},
  issn = {21506000},
  doi = {10.1177/0022146519870521},
  abstract = {This study uses recent data to investigate if smoking initiation diffuses through friendship networks over the high school period and explores if diffusion processes differ across schools. One thousand four hundred and twenty-five racially and ethnically diverse youth from four high schools in Los Angeles were surveyed four times over the high school period from 2010 to 2013. Probit regression models and stochastic actor-based models for network dynamics tested for peer effects on smoking initiation. Friend smoking was found to predict adolescent smoking, and smoking initiation diffused through friendship networks in some but not all of the schools. School differences in smoking rates and the popularity of smokers may be linked to differences in the diffusion of smoking through peer networks. We conclude that there are differences in peer effects on smoking initiation across schools that will be important to account for in network-based smoking interventions.},
  keywords = {adolescent,diffusion,high school,smoking initiation,social network,stochastic actor-based model}
}

@article{valenteDiffusionContagionProcesses2020,
  title = {Diffusion/{{Contagion Processes}} on {{Social Networks}}},
  author = {Valente, Thomas W. and {Vega Yon}, George G.},
  year = {2020},
  month = apr,
  journal = {Health Education \& Behavior},
  volume = {47},
  number = {2},
  pages = {235--248},
  issn = {1090-1981},
  doi = {10.1177/1090198120901497},
  abstract = {This study models how new ideas, practices, or diseases spread within and between communities, the diffusion of innovations or contagion. Several factors affect diffusion such as the characteristics of the initial adopters, the seeds; the structure of the network over which diffusion occurs; and the shape of the threshold distribution, which is the proportion of prior adopting peers needed for the focal individual to adopt. In this study, seven seeding conditions are modeled: (1) three opinion leadership indicators, (2) two bridging measures, (3) marginally positioned seeds, and (4) randomly selected seeds for comparison. Three network structures are modeled: (1) random, (2) small-world, and (3) scale-free. Four threshold distributions are modeled: (1) normal; (2) uniform; (3) beta 7,14; and (4) beta 1,2; all of which have a mean threshold of 33\%, with different variances. The results show that seeding with nodes high on in-degree centrality and/or inverse constraint has faster and more widespread diffusion. Random networks had faster and higher prevalence of diffusion than scale-free ones, but not different from small-world ones. Compared with the normal threshold distribution, the uniform one had faster diffusion and the beta 7,14 distribution had slower diffusion. Most significantly, the threshold distribution standard deviation was associated with rate and prevalence such that higher threshold standard deviations accelerated diffusion and increased prevalence. These results underscore factors that health educators and public health advocates should consider when developing interventions or trying to understand the potential for behavior change.}
}

@article{valenteNetworkInfluencesPolicy2019,
  title = {Network Influences on Policy Implementation: {{Evidence}} from a Global Health Treaty},
  author = {Valente, Thomas W. and Wipfli, Heather and {Vega Yon}, George G.},
  year = {2019},
  journal = {Social Science and Medicine},
  issn = {18735347},
  doi = {10.1016/j.socscimed.2019.01.008},
  abstract = {This paper examines whether country implementation of a public health treaty is influenced by the implementation behaviors of other countries to which they have network ties. We examine implementation of the Framework Convention on Tobacco Control (FCTC) adopted by the World Health Organization in 2003 and ratified by approximately 94\% of countries as of 2016. We constructed five networks: (1) geographic distance, (2) general trade, (3) tobacco trade, (4) GLOBALink referrals, and (5) GLOBALink co-subscriptions. Network exposure terms were constructed from these networks based on the implementation scores for six articles of the FCTC treaty. We estimate effects using a lagged Type 1 Tobit model. Results show that network effects were significant: (a) across all networks for article 6 (pricing and taxation), (b) distance, general trade, GL referrals, and GL co-subscriptions for article 8 (second hand smoke), (c) distance, general trade, and GL co-subscriptions for article 11 (packaging and labeling), and (d) distance and GL co-subscription for article 13 (promotion and advertising), (e) tobacco trade and GL co-subscriptions for article 14 (cessation). These results indicate that diffusion effects were more prevalent for pricing and taxation as well as restrictions on smoking in public places and packaging and labeling. These results suggest that network influences are possible in domains that are amenable to control by national governments but unlikely to occur in domains established by existing regulatory systems. Implications for future studies of policy implementation are discussed.},
  keywords = {Diffusion of innovations,Policy implementation,Social network analysis,Tobacco control}
}

@article{lesageIntroductionSpatialEconometrics2008,
  title = {An {{Introduction}} to {{Spatial Econometrics}}},
  author = {LeSage, James P.},
  year = {2008},
  journal = {Revue d'{\'e}conomie industrielle},
  volume = {123},
  number = {123},
  pages = {19--44},
  issn = {0154-3229},
  doi = {10.4000/rei.3887},
  abstract = {An introduction to spatial econometric models and methods is provided that discusses spatial autoregressive processes that can be used to extend conventional regression models. Estimation and interpretation of these models are illustrated with an applied example that examines the relationship between commuting to work times and transportation mode choice for a sample of 3,110 US counties in the year 2000. These extensions to conventional regression models are useful when modeling cross-sectional regional observations or and panel data samples collected from regions over both space and time can be easily implemented using publicly available software. Use of these models for the case of non-spatial structured dependence is also discussed.},
  isbn = {978-1420064247},
  pmid = {578345366},
  keywords = {d{\'e}pendance spatiale,{\'e}conom{\'e}trie spatiale,processus spatial autor{\'e}gressif,Spatial Autoregressive Processes,Spatial Dependence,Spatial Econometrics}
}

@article{lesageBiggestMythSpatial2014,
  title = {The {{Biggest Myth}} in {{Spatial Econometrics}}},
  author = {LeSage, James P. and Pace, R. Kelley},
  year = {2014},
  journal = {Econometrics},
  volume = {2},
  number = {4},
  pages = {217--249},
  issn = {1556-5068},
  doi = {10.2139/ssrn.1725503},
  abstract = {There is near universal agreement that estimates and inferences from spatial regression models are sensitive to particular specifications used for the spatial weight structure in these models. We find little theoretical basis for this commonly held belief, if estimates and inferences are based on the true partial derivatives for a well-specified spatial regression model. We conclude that this myth may have arisen from past applied work that incorrectly interpreted the model coefficients as if they were partial derivatives, or from use of mis-specified models.},
  keywords = {direct and indirect effects estimates,sensitivity to spatial weights}
}

@article{bellSensingEatingMimicry2019,
  title = {Sensing Eating Mimicry among Family Members},
  author = {Bell, Brooke M. and {Spruijt-Metz}, Donna and Vega Yon, George G. and Mondol, Abu S. and Alam, Ridwan and Ma, Meiyi and Emi, Ifat and Lach, John and Stankovic, John A. and Haye, Kayla De La},
  year = {2019},
  journal = {Translational Behavioral Medicine},
  issn = {16139860},
  doi = {10.1093/tbm/ibz051},
  abstract = {Family relationships influence eating behavior and health outcomes (e.g., obesity). Because eating is often habitual (i.e., automatically driven by external cues), unconscious behavioral mimicry may be a key interpersonal influence mechanism for eating within families. This pilot study extends existing literature on eating mimicry by examining whether multiple family members mimicked each other's bites during natural meals. Thirty-Three participants from 10 families were videotaped while eating an unstructured family meal in a kitchen lab setting. Videotapes were coded for participants' bite occurrences and times. We tested whether the likelihood of a participant taking a bite increased when s/he was externally cued by a family eating partner who had recently taken a bite (i.e., bite mimicry). A paired-sample t-Test indicated that participants had a significantly faster eating rate within the 5 s following a bite by their eating partner, compared to their bite rate at other times (t = 7.32, p {$<$} .0001). Nonparametric permutation testing identified five of 78 dyads in which there was significant evidence of eating mimicry; and 19 of 78 dyads that had p values {$<$} .1. This pilot study provides preliminary evidence that suggests eating mimicry may occur among a subset of family members, and that there may be types of family ties more prone to this type of interpersonal influence during meals.},
  keywords = {Eating,Family,Mimicry,Obesity,Permutation tests,Social influence}
}

@article{tanakaImaginaryNetworkMotifs2024,
  title = {Imaginary Network Motifs: {{Structural}} Patterns of False Positives and Negatives in Social Networks},
  shorttitle = {Imaginary Network Motifs},
  author = {Tanaka, Kyosuke and Vega Yon, George G.},
  year = {2024},
  month = jul,
  journal = {Social Networks},
  volume = {78},
  pages = {65--80},
  issn = {0378-8733},
  doi = {10.1016/j.socnet.2023.11.005},
  urldate = {2023-12-08},
  abstract = {We examine the structural patterns in the cognitive representation of social networks by systematically classifying false positives and negatives. Although existing literature on Cognitive Social Structures (CSS) has begun exploring false positives and negatives by comparing actual and perceived networks, it has not differentiated simultaneous occurrences of true and false positives and negatives on network motifs, such as reciprocity and triadic closure. Here, we propose a theoretical framework to categorize three classes of errors we call imaginary network motifs as combinations of accurately and erroneously perceived ties: (a) partially false, (b) completely false, and (c) mixed false. Using four published CSS data sets, we empirically test which imaginary network motifs are significantly more or less present in different types of perceived networks than the corresponding actual networks. Our results confirm that people not only fill in the blanks as suggested in the prior research but also conceive other imaginary structures. The findings advance our understanding of perception gaps between actual and perceived networks and have implications for designing more accurate network modeling and sampling.},
  keywords = {Cognitive errors,Cognitive Social Structures,Network motifs,Network perceptions,Social networks}
}

@article{robinsNetworkModelsSocial2001b,
  title = {Network Models for Social Influence Processes},
  author = {Robins, Garry and Pattison, Philippa and Elliott, Peter},
  year = {2001},
  month = jun,
  journal = {Psychometrika},
  volume = {66},
  number = {2},
  pages = {161--189},
  issn = {1860-0980},
  doi = {10.1007/BF02294834},
  urldate = {2023-12-30},
  abstract = {This paper generalizes thep* class of models for social network data to predict individual-level attributes from network ties. Thep* model for social networks permits the modeling of social relationships in terms of particular local relational or network configurations. In this paper we present methods for modeling attribute measures in terms of network ties, and so constructp* models for the patterns of social influence within a network. Attribute variables are included in a directed dependence graph and the Hammersley-Clifford theorem is employed to derive probability models whose parameters can be estimated using maximum pseudo-likelihood. The models are compared to existing network effects models. They can be interpreted in terms of public or private social influence phenomena within groups. The models are illustrated by an empirical example involving a training course, with trainees' reactions to aspects of the course found to relate to those of their network partners.},
  langid = {english},
  keywords = {attitudes,graphical models,network effects models,p* models,social influence,social networks},
  file = {/home/george/Zotero/storage/UXVNIKTU/Robins et al. - 2001 - Network models for social influence processes.pdf}
}


@Article{Bivand2022,
  author = {{Roger Bivand}},
  title = {R Packages for Analyzing Spatial Data: A Comparative Case Study with Areal Data},
  journal = {Geographical Analysis},
  year = {2022},
  volume = {54},
  number = {3},
  pages = {488-518},
  doi = {10.1111/gean.12319},
}

@Manual{Handcock2023,
  author = {Mark S. Handcock and David R. Hunter and Carter T. Butts and Steven M. Goodreau and Pavel N. Krivitsky and Martina Morris},
  title = {ergm: Fit, Simulate and Diagnose Exponential-Family Models for Networks},
  organization = {The Statnet Project (\url{https://statnet.org})},
  year = {2023},
  note = {R package version 4.5-7213},
  url = {https://CRAN.R-project.org/package=ergm},
}

@Article{Hunter2008,
  title = {{ergm}: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks},
  author = {David R. Hunter and Mark S. Handcock and Carter T. Butts and Steven M. Goodreau and Martina Morris},
  journal = {Journal of Statistical Software},
  year = {2008},
  volume = {24},
  number = {3},
  pages = {1--29},
  doi = {10.18637/jss.v024.i03},
}

@article{wangUnderstandingNetworksExponentialfamily2023,
  title = {Understanding Networks with Exponential-Family Random Network Models},
  author = {Wang, Zeyi and Fellows, Ian E. and Handcock, Mark S.},
  year = {2023},
  month = aug,
  journal = {Social Networks},
  pages = {S0378873323000497},
  issn = {03788733},
  doi = {10.1016/j.socnet.2023.07.003},
  urldate = {2023-08-10},
  langid = {english},
  file = {/home/george/Zotero/storage/EHHVG7U3/Wang et al. - 2023 - Understanding networks with exponential-family ran.pdf}
}

@phdthesis{ernmsFellows2023,
author={Fellows,Ian E.},
year={2012},
title={Exponential Family Random Network Models},
journal={ProQuest Dissertations and Theses},
pages={104},
note={Copyright - Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works; Last updated - 2023-03-04},
abstract={Random graphs, where the presence of connections between nodes are considered random variables, have wide applicability in the social sciences. Exponential-family Random Graph Models (ERGM) have shown themselves to be a useful class of models for representing complex social phenomena. We generalize ERGM by also modeling nodal attributes as random variates, thus creating a random model of the full network, which we call Exponential-family Random Network Models (ERNM). We demonstrate how this framework allows a new formulation for logistic regression in network data. We develop likelihood-based inference for the model in the case of a fully observed network and an MCMC algorithm to implement it. We then develop a theory of inference for ERNM when only part of the network is observed, as well as specific methodology for missing data, including non-ignorable mechanisms for network-based sampling designs and for latent class models. We also consider contact tracing sampling designs which are of considerable importance to infectious disease epidemiology and public health. This culminates in a treatment of respondent driven sampling (RDS), which is a widely used link tracing design.},
keywords={Pure sciences; Exponential family; Mcmc-mle; Random graph; Random networks; Statistics; 0463:Statistics},
isbn={978-1-267-74530-9},
language={English},
url={https://login.ezproxy.lib.utah.edu/login?url=https://www.proquest.com/dissertations-theses/exponential-family-random-network-models/docview/1221548720/se-2},
}

@misc{BasicDefinitionsConcepts2014,
  title = {1.1: {{Basic Definitions}} and {{Concepts}}},
  shorttitle = {1.1},
  year = {2014},
  month = apr,
  journal = {Statistics LibreTexts},
  urldate = {2024-01-06},
  abstract = {Statistics is a study of data: describing properties of data (descriptive statistics) and drawing conclusions about a population based on information in a sample (inferential statistics). The {\ldots}},
  howpublished = {https://stats.libretexts.org/Bookshelves/Introductory\_Statistics/Introductory\_Statistics\_(Shafer\_and\_Zhang)/01\%3A\_Introduction\_to\_Statistics/1.01\%3A\_Basic\_Definitions\_and\_Concepts},
  langid = {english},
  file = {/home/george/Zotero/storage/T63D8XJF/1.html}
}

@article{greenlandStatisticalTestsValues2016,
  title = {Statistical Tests, {{P}} Values, Confidence Intervals, and Power: A Guide to Misinterpretations},
  shorttitle = {Statistical Tests, {{P}} Values, Confidence Intervals, and Power},
  author = {Greenland, Sander and Senn, Stephen J. and Rothman, Kenneth J. and Carlin, John B. and Poole, Charles and Goodman, Steven N. and Altman, Douglas G.},
  year = {2016},
  month = apr,
  journal = {European Journal of Epidemiology},
  volume = {31},
  number = {4},
  pages = {337--350},
  issn = {1573-7284},
  doi = {10.1007/s10654-016-0149-3},
  urldate = {2024-01-06},
  abstract = {Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, correct, and foolproof. Instead, correct use and interpretation of these statistics requires an attention to detail which seems to tax the patience of working scientists. This high cognitive demand has led to an epidemic of shortcut definitions and interpretations that are simply wrong, sometimes disastrously so{\textemdash}and yet these misinterpretations dominate much of the scientific literature. In light of this problem, we provide definitions and a discussion of basic statistics that are more general and critical than typically found in traditional introductory expositions. Our goal is to provide a resource for instructors, researchers, and consumers of statistics whose knowledge of statistical theory and technique may be limited but who wish to avoid and spot misinterpretations. We emphasize how violation of often unstated analysis protocols (such as selecting analyses for presentation based on the P values they produce) can lead to small P values even if the declared test hypothesis is correct, and can lead to large P values even if that hypothesis is incorrect. We then provide an explanatory list of 25 misinterpretations of P values, confidence intervals, and power. We conclude with guidelines for improving statistical interpretation and reporting.},
  langid = {english},
  keywords = {Confidence intervals,Hypothesis testing,Null testing,P value,Power,Significance tests,Statistical testing},
  file = {/home/george/Zotero/storage/BR4IUFPN/Greenland et al. - 2016 - Statistical tests, P values, confidence intervals,.pdf}
}

@article{gelmanFailureNullHypothesis2018,
  title = {The {{Failure}} of {{Null Hypothesis Significance Testing When Studying Incremental Changes}}, and {{What}} to {{Do About It}}},
  author = {Gelman, Andrew},
  year = {2018},
  month = jan,
  journal = {Personality and Social Psychology Bulletin},
  volume = {44},
  number = {1},
  pages = {16--23},
  issn = {0146-1672, 1552-7433},
  doi = {10.1177/0146167217729162},
  urldate = {2024-01-06},
  abstract = {A standard mode of inference in social and behavioral science is to establish stylized facts using statistical significance in quantitative studies. However, in a world in which measurements are noisy and effects are small, this will not work: selection on statistical significance leads to effect sizes which are overestimated and often in the wrong direction. After a brief discussion of two examples, one in economics and one in social psychology, we consider the procedural solution of open postpublication review, the design solution of devoting more effort to accurate measurements and within-person comparisons, and the statistical analysis solution of multilevel modeling and reporting all results rather than selection on significance. We argue that the current replication crisis in science arises in part from the ill effects of null hypothesis significance testing being used to study small effects with noisy data. In such settings, apparent success comes easy but truly replicable results require a more serious connection between theory, measurement, and data.},
  langid = {english},
  file = {/home/george/Zotero/storage/FATSXDT4/Gelman - 2018 - The Failure of Null Hypothesis Significance Testin.pdf}
}

@book{casellaStatisticalInference2021,
  title = {Statistical {{Inference}}},
  author = {Casella, George and Berger, Roger L.},
  year = {2021},
  month = jan,
  publisher = {{Cengage Learning}},
  abstract = {This book builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts. Intended for first-year graduate students, this book can be used for students majoring in statistics who have a solid mathematics background. It can also be used in a way that stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures for a variety of situations, and less concerned with formal optimality investigations.Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.},
  googlebooks = {FAUVEAAAQBAJ},
  isbn = {978-0-357-75313-2},
  langid = {english},
  keywords = {Mathematics / Probability \& Statistics / General}
}

@article{geyerConstrainedMonteCarlo1992,
  title = {Constrained {{Monte Carlo Maximum Likelihood}} for {{Dependent Data}}},
  author = {Geyer, Charles J. and Thompson, Elizabeth A.},
  year = {1992},
  journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
  volume = {54},
  number = {3},
  eprint = {2345852},
  eprinttype = {jstor},
  pages = {657--699},
  publisher = {{[Royal Statistical Society, Wiley]}},
  issn = {0035-9246},
  urldate = {2024-01-07},
  abstract = {Maximum likelihood estimates (MLEs) in autologistic models and other exponential family models for dependent data can be calculated with Markov chain Monte Carlo methods (the Metropolis algorithm or the Gibbs sampler), which simulate ergodic Markov chains having equilibrium distributions in the model. From one realization of such a Markov chain, a Monte Carlo approximant to the whole likelihood function can be constructed. The parameter value (if any) maximizing this function approximates the MLE. When no parameter point in the model maximizes the likelihood, the MLE in the closure of the exponential family may exist and can be calculated by a two-phase algorithm, first finding the support of the MLE by linear programming and then finding the distribution within the family conditioned on the support by maximizing the likelihood for that family. These methods are illustrated by a constrained autologistic model for DNA fingerprint data. MLEs are compared with maximum pseudolikelihood estimates (MPLEs) and with maximum conditional likelihood estimates (MCLEs), neither of which produce acceptable estimates, the MPLE because it overestimates dependence, and the MCLE because conditioning removes the constraints.},
  file = {/home/george/Zotero/storage/F2EKQLP2/Geyer and Thompson - 1992 - Constrained Monte Carlo Maximum Likelihood for Dep.pdf}
}

@article{koskinenOutliersInfluentialObservations2018,
  title = {Outliers and {{Influential Observations}} in {{Exponential Random Graph Models}}},
  author = {Koskinen, Johan and Wang, Peng and Robins, Garry and Pattison, Philippa},
  year = {2018},
  month = dec,
  journal = {Psychometrika},
  volume = {83},
  number = {4},
  pages = {809--830},
  issn = {0033-3123, 1860-0980},
  doi = {10.1007/s11336-018-9635-8},
  urldate = {2024-01-09},
  langid = {english},
  file = {/home/george/Zotero/storage/FV9MDZM6/Koskinen et al. - 2018 - Outliers and Influential Observations in Exponenti.pdf}
}

@article{koskinenAnalysingNetworksNetworks2023,
  title = {Analysing Networks of Networks},
  author = {Koskinen, Johan and Jones, Pete and Medeuov, Darkhan and Antonyuk, Artem and Puzyreva, Kseniia and Basov, Nikita},
  year = {2023},
  month = jul,
  journal = {Social Networks},
  volume = {74},
  pages = {102--117},
  issn = {0378-8733},
  doi = {10.1016/j.socnet.2023.02.002},
  urldate = {2024-01-09},
  abstract = {We consider data with multiple observations or reports on a network in the case when these networks themselves are connected through some form of network ties. We could take the example of a cognitive social structure where there is another type of tie connecting the actors that provide the reports; or the study of interpersonal spillover effects from one cultural domain to another facilitated by the social ties. Another example is when the individual semantic structures are represented as semantic networks of a group of actors and connected through these actors' social ties to constitute knowledge of a social group. How to jointly represent the two types of networks is not trivial as the layers and not the nodes of the layers of the reported networks are coupled through a network on the reports. We propose to transform the different multiple networks using line graphs, where actors are affiliated with ties represented as nodes, and represent the totality of the different types of ties as a multilevel network. This affords studying the associations between the social network and the reports as well as the alignment of the reports to a criterion graph. We illustrate how the procedure can be applied to studying the social construction of knowledge in local flood management groups. Here we use multilevel exponential random graph models but the representation also lends itself to stochastic actor-oriented models, multilevel blockmodels, and any model capable of handling multilevel networks.},
  keywords = {Multigraphs,Multilevel networks,Multiplex networks,Sociosemantic networks},
  file = {/home/george/Zotero/storage/KPU8REVQ/Koskinen et al. - 2023 - Analysing networks of networks.pdf}
}

@article{leifeldTexregConversionStatistical2013,
  title = {{\textbf{Texreg}} : {{Conversion}} of {{Statistical Model Output}} in {{{\emph{R}}}} to {{L A T E X}} and {{HTML Tables}}},
  shorttitle = {{\textbf{Texreg}}},
  author = {Leifeld, Philip},
  year = {2013},
  journal = {Journal of Statistical Software},
  volume = {55},
  number = {8},
  issn = {1548-7660},
  doi = {10.18637/jss.v055.i08},
  urldate = {2024-01-09},
  abstract = {A recurrent task in applied statistics is the (mostly manual) preparation of model output for inclusion in LATEX, Microsoft Word, or HTML documents {\textendash} usually with more than one model presented in a single table along with several goodness-of-fit statistics. However, statistical models in R have diverse object structures and summary methods, which makes this process cumbersome. This article first develops a set of guidelines for converting statistical model output to LATEX and HTML tables, then assesses to what extent existing packages meet these requirements, and finally presents the texreg package as a solution that meets all of the criteria set out in the beginning. After providing various usage examples, a blueprint for writing custom model extensions is proposed.},
  langid = {english},
  file = {/home/george/Zotero/storage/J2E7L5H9/Leifeld - 2013 - texreg  Conversion of Statistical Model Ou.pdf}
}

@article{Robins2007,
author = {Robins, Garry and Pattison, Pip and Kalish, Yuval and Lusher, Dean},
doi = {10.1016/j.socnet.2006.08.002},
journal = {Social Networks},
keywords = {Exponential random graph models,Statistical models for social networks,p* models},
number = {2},
pages = {173--191},
pmid = {18449326},
title = {{An introduction to exponential random graph (p*) models for social networks}},
volume = {29},
year = {2007}
}

@article{Holland1981,
author = {Holland, Paul W. and Leinhardt, Samuel},
doi = {10.2307/2287037},
journal = {Journal of the American Statistical Association},
keywords = {generalized iterative scaling,networks,random digraphs,sociome-,try},
number = {373},
pages = {33--50},
title = {{An exponential family of probability distributions for directed graphs}},
volume = {76},
year = {1981}
}

@article{Frank1986,
	abstract = {Log-linear statistical models are used to characterize ran- dom graphs with general dependence structure and with Markov dependence. Sufficient statistics for Markov graphs are shown to be given by counts of various triangles and stars. In particular, we show under which assumptions the triad counts are sufficient statistics. We discuss inference methodology for some simple Markov graphs.},
	author = {Frank, O and Strauss, David},
	doi = {10.2307/2289017},
	journal = {Journal of the American Statistical Association},
	keywords = {log-linear network model,markov field},
	mendeley-groups = {network dependence,ergms},
	number = {395},
	pages = {832--842},
	pmid = {7439394},
	title = {{Markov graphs}},
	url = {http://amstat.tandfonline.com/doi/abs/10.1080/01621459.1986.10478342},
	volume = {81},
	year = {1986}
}

@article{Wasserman1996,
author = {Wasserman, Stanley and Pattison, Philippa},
doi = {10.1007/BF02294547},
journal = {Psychometrika},
keywords = {categorical data analysis,random graphs,social network analysis},
number = {3},
pages = {401--425},
pmid = {10613111},
title = {{Logit models and logistic regressions for social networks: I. An introduction to Markov graphs and p*}},
volume = {61},
year = {1996}
}

@article{Snijders2006,
    author = {Snijders, Tom A B and Pattison, Philippa E and Robins, Garry L and Handcock, Mark S},
    doi = {10.1111/j.1467-9531.2006.00176.x},
    issn = {0081-1750},
    journal = {Sociological Methodology},
    month = {12},
    number = {1},
    pages = {99--153},
    title = {{New specifications for exponential random graph models}},
    url = {http://www.jstor.org/stable/25046693 http://smx.sagepub.com/lookup/doi/10.1111/j.1467-9531.2006.00176.x},
    volume = {36},
    year = {2006}
}

@article{hunterCurvedExponentialFamily2007,
  title = {Curved Exponential Family Models for Social Networks},
  author = {Hunter, David R.},
  year = {2007},
  journal = {Social Networks},
  volume = {29},
  number = {2},
  pages = {216--230},
  issn = {03788733},
  doi = {10.1016/j.socnet.2006.08.005},
  abstract = {Curved exponential family models are a useful generalization of exponential random graph models (ERGMs). In particular, models involving the alternating k-star, alternating k-triangle, and alternating k-twopath statistics of Snijders et al. [Snijders, T.A.B., Pattison, P.E., Robins, G.L., Handcock, M.S., in press. New specifications for exponential random graph models. Sociological Methodology] may be viewed as curved exponential family models. This article unifies recent material in the literature regarding curved exponential family models for networks in general and models involving these alternating statistics in particular. It also discusses the intuition behind rewriting the three alternating statistics in terms of the degree distribution and the recently introduced shared partner distributions. This intuition suggests a redefinition of the alternating k-star statistic. Finally, this article demonstrates the use of the statnet package in R for fitting models of this sort, comparing new results on an oft-studied network dataset with results found in the literature. {\textcopyright} 2006 Elsevier B.V. All rights reserved.},
  isbn = {0378-8733},
  pmid = {18311321},
  keywords = {Exponential random graph model,Maximum likelihood estimation,p-Star model},
  file = {/home/george/Zotero/storage/3SUJVC4R/Hunter - 2007 - Curved exponential family models for social networ.pdf}
}

@article{Slaughter2016,
   abstract = {In many applications, researchers may be interested in studying patterns of dyadic relationships that involve multiple groups, with a focus on modeling the systematic patterns within groups and how these structural patterns differ across groups. A number of different models - many of them potentially quite powerful - have been developed that allow for researchers to study these differences. However, as with any set of models, these are limited in ways that constrain the types of questions researchers may ask, such as those involving the variance in group-wise structural features. In this paper, we demonstrate some of the ways in which multilevel models based on a hierarchical Bayesian approach might be used to further develop and extend existing exponential random graph models to address such constraints. These include random coefficient extensions to the standard ERGM for sets of multiple unconnected or connected networks and examples of multilevel models that allow for the estimation of structural entrainment among connected groups. We demonstrate the application of these models to real-world and simulated data sets.},
   author = {Andrew J. Slaughter and Laura M. Koehly},
   doi = {10.1016/j.socnet.2015.11.002},
   issn = {03788733},
   journal = {Social Networks},
   keywords = {Autoregressive models,Exponential random graph models,Hierarchical models,Multilevel networks},
   pages = {334-345},
   publisher = {Elsevier B.V.},
   title = {Multilevel models for social networks: Hierarchical Bayesian approaches to exponential random graph modeling},
   volume = {44},
   url = {http://dx.doi.org/10.1016/j.socnet.2015.11.002},
   year = {2016},
}

@Manual{ergmmulti,
    author = {Pavel N. Krivitsky},
    title = {ergm.multi: Fit, Simulate and Diagnose Exponential-Family Models for Multiple or Multilayer Networks},
    organization = {The Statnet Project (\url{https://statnet.org})},
    year = {2023},
    note = {R package version 0.2.0},
    url = {https://CRAN.R-project.org/package=ergm.multi},
  }

@article{stadtfeldStatisticalPowerLongitudinal2020,
  title = {Statistical {{Power}} in {{Longitudinal Network Studies}}},
  author = {Stadtfeld, Christoph and Snijders, Tom A. B. and Steglich, Christian and {van Duijn}, Marijtje},
  year = {2020},
  month = nov,
  journal = {Sociological Methods \& Research},
  volume = {49},
  number = {4},
  pages = {1103--1132},
  publisher = {{SAGE Publications Inc}},
  issn = {0049-1241},
  doi = {10.1177/0049124118769113},
  urldate = {2024-01-12},
  abstract = {Longitudinal social network studies can easily suffer from insufficient statistical power. Studies that simultaneously investigate change of network ties and change of nodal attributes (selection and influence studies) are particularly at risk because the number of nodal observations is typically much lower than the number of observed tie variables. This article presents a simulation-based procedure to evaluate statistical power of longitudinal social network studies in which stochastic actor-oriented models are to be applied. Two detailed case studies illustrate how statistical power is strongly affected by network size, number of data collection waves, effect sizes, missing data, and participant turnover. These issues should thus be explored in the design phase of longitudinal social network studies.},
  file = {/home/george/Zotero/storage/IUTE2DZC/Stadtfeld et al. - 2020 - Statistical Power in Longitudinal Network Studies.pdf}
}

@article{karrerStochasticBlockmodelsCommunity2011,
  title = {Stochastic Blockmodels and Community Structure in Networks},
  author = {Karrer, Brian and Newman, M. E. J.},
  year = {2011},
  month = jan,
  journal = {Physical Review E},
  volume = {83},
  number = {1},
  pages = {016107},
  issn = {1539-3755, 1550-2376},
  doi = {10.1103/PhysRevE.83.016107},
  urldate = {2024-01-07},
  langid = {english},
  file = {/home/george/Zotero/storage/3YMCBSW5/Karrer and Newman - 2011 - Stochastic blockmodels and community structure in .pdf}
}

@article{koskinenBayesianAnalysisSocial2022,
  title = {Bayesian {{Analysis}} of {{Social Influence}}},
  author = {Koskinen, Johan and Daraganova, Galina},
  year = {2022},
  month = oct,
  journal = {Journal of the Royal Statistical Society Series A: Statistics in Society},
  volume = {185},
  number = {4},
  pages = {1855--1881},
  issn = {0964-1998, 1467-985X},
  doi = {10.1111/rssa.12844},
  urldate = {2024-01-12},
  abstract = {The network influence model is a model for binary outcome variables that accounts for dependencies between outcomes for units that are relationally tied. The basic influence model was previously extended to afford a suite of new dependence assumptions and because of its relation to traditional Markov random field models it is often referred to as the auto logistic actor-attribute model (ALAAM). We extend on current approaches for fitting ALAAMs by presenting a comprehensive Bayesian inference scheme that supports testing of dependencies across subsets of data and the presence of missing data. We illustrate different aspects of the procedures through three empirical examples: masculinity attitudes in an all-male Australian school class, educational progression in Swedish schools, and unemployment among adults in a community sample in Australia.},
  langid = {english},
  file = {/home/george/Zotero/storage/2NSB84E3/Koskinen and Daraganova - 2022 - Bayesian Analysis of Social Influence.pdf}
}

@article{buttsRelationalEventModels2023,
  title = {Relational Event Models in Network Science},
  author = {Butts, Carter T. and Lomi, Alessandro and Snijders, Tom A. B. and Stadtfeld, Christoph},
  year = {2023},
  month = jun,
  journal = {Network Science},
  volume = {11},
  number = {2},
  pages = {175--183},
  issn = {2050-1242, 2050-1250},
  doi = {10.1017/nws.2023.9},
  urldate = {2023-08-09},
  abstract = {Abstract             Relational event models (REMs) for the analysis of social interaction were first introduced 15 years ago. Since then, a number of important substantive and methodological contributions have produced their progressive refinement and hence facilitated their increased adoption in studies of social and other networks. Today REMs represent a well-established class of statistical models for relational processes. This special issue of Network Science demonstrates the standing and recognition that REMs have achieved within the network analysis and networks science communities. We wrote this brief introductory editorial essay with four main objectives in mind: (i) positioning relational event data and models in the larger context of contemporary network science and social network research; (ii) reviewing some of the most important recent developments; (iii) presenting the innovative studies collected in this special issue as evidence of the empirical value of REMs, and (iv) identifying open questions and future research directions.},
  langid = {english},
  file = {/home/george/Zotero/storage/RVWY9HWP/Butts et al. - 2023 - Relational event models in network science.pdf}
}

@article{krivitskyUsingContrastiveDivergence2017,
  title = {Using Contrastive Divergence to Seed {{Monte Carlo MLE}} for Exponential-Family Random Graph Models},
  author = {Krivitsky, Pavel N.},
  year = {2017},
  month = mar,
  journal = {Computational Statistics \& Data Analysis},
  volume = {107},
  pages = {149--161},
  issn = {0167-9473},
  doi = {10.1016/j.csda.2016.10.015},
  urldate = {2024-01-12},
  abstract = {Exponential-family models for dependent data have applications in a wide variety of areas, but the dependence often results in an intractable likelihood, requiring either analytic approximation or MCMC-based techniques to fit, the latter requiring an initial parameter configuration to seed their simulations. A poor initial configuration can lead to slow convergence or outright failure. The approximate techniques that could be used to find them tend not to be as general as the simulation-based and require implementation separate from that of the MLE-finding algorithm. Contrastive divergence is a more recent simulation-based approximation technique that uses a series of abridged MCMC runs instead of running them to stationarity. Combining it with the importance sampling Monte Carlo MLE yields a method for obtaining adequate initial values that is applicable to a wide variety of modeling scenarios. Practical issues such as stopping criteria and selection of tuning parameters are also addressed. A simple generalization of the Monte Carlo MLE partial stepping algorithm to curved exponential families (applicable to MLE-finding as well) is also proposed. The proposed approach reuses the aspects of an MLE implementation that are model-specific, so little to no additional implementer effort is required to obtain adequate initial parameters. This is demonstrated on a series of network datasets and models drawn from exponential-family random graph model computation literature, also exploring the limitations of the techniques considered.},
  keywords = {Curved exponential family,ERGM,Network data,Partial stepping},
  file = {/home/george/Zotero/storage/WEG2VMPC/Krivitsky - 2017 - Using contrastive divergence to seed Monte Carlo M.pdf}
}

@article{hunterComputationalStatisticalMethods2012,
  title = {Computational Statistical Methods for Social Network Models},
  author = {Hunter, David R. and Krivitsky, Pavel N. and Schweinberger, Michael},
  year = {2012},
  journal = {Journal of Computational and Graphical Statistics},
  volume = {21},
  number = {4},
  pages = {856--882},
  issn = {15372715},
  doi = {10.1080/10618600.2012.732921},
  abstract = {We review the broad range of recent statistical work in social network models, with emphasis on computational aspects of these methods. Particular focus is applied to exponential-family random graph models (ERGM) and latent variable models for data on complete networks observed at a single time point, though we also briefly review many methods for incompletely observed networks and networks observed at multiple time points. Although we mention far more modeling techniques than we can possibly cover in depth, we provide numerous citations to current literature. We illustrate several of the methods on a small, well-known network dataset, Sampson's monks, providing code where possible so that these analyses may be duplicated. {\textcopyright} 2012 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.},
  keywords = {Degeneracy,ERGM,Latent variables,MCMC MLE,Variational methods},
  file = {/home/george/Zotero/storage/BDMRLLTI/Hunter et al. - 2012 - Computational statistical methods for social netwo.pdf}
}

@misc{stivalaUsingSampledNetwork2020,
  title = {Using {{Sampled Network Data With The Autologistic Actor Attribute Model}}},
  author = {Stivala, Alex D. and Gallagher, H. Colin and Rolls, David A. and Wang, Peng and Robins, Garry L.},
  year = {2020},
  month = feb,
  number = {arXiv:2002.00849},
  eprint = {2002.00849},
  primaryclass = {cs, stat},
  publisher = {{arXiv}},
  doi = {10.48550/arXiv.2002.00849},
  urldate = {2024-01-12},
  abstract = {Social science research increasingly benefits from statistical methods for understanding the structured nature of social life, including for social network data. However, the application of statistical network models within large-scale community research is hindered by too little understanding of the validity of their inferences under realistic data collection conditions, including sampled or missing network data. The autologistic actor attribute model (ALAAM) is a statistical model based on the well-established exponential random graph model (ERGM) for social networks. ALAAMs can be regarded as a social influence model, predicting an individual-level outcome based on the actor's network ties, concurrent outcomes of his/her network partners, and attributes of the actor and his/her network partners. In particular, an ALAAM can be used to measure contagion effects, that is, the propensity of two actors connected by a social network tie to both have the same value of an attribute. We investigate the effect of using simple random samples and snowball samples of network data on ALAAM parameter inference, and find that parameter inference can still work well even with a nontrivial fraction of missing nodes. However it is safer to take a snowball sample of the network and estimate conditional on the snowball sampling structure.},
  archiveprefix = {arxiv},
  keywords = {Computer Science - Social and Information Networks,Statistics - Methodology},
  file = {/home/george/Zotero/storage/ADMIG3P4/Stivala et al. - 2020 - Using Sampled Network Data With The Autologistic A.pdf;/home/george/Zotero/storage/9GB42DWP/2002.html}
}

@incollection{brandenbergerInterdependenciesConflictDynamics2020,
  title = {Interdependencies in {{Conflict Dynamics}}: {{Analyzing Endogenous Patterns}} in {{Conflict Event Data Using Relational Event Models}}},
  shorttitle = {Interdependencies in {{Conflict Dynamics}}},
  booktitle = {Computational {{Conflict Research}}},
  author = {Brandenberger, Laurence},
  editor = {Deutschmann, Emanuel and Lorenz, Jan and Nardin, Luis G. and Natalini, Davide and Wilhelm, Adalbert F. X.},
  year = {2020},
  series = {Computational {{Social Sciences}}},
  pages = {67--80},
  publisher = {{Springer International Publishing}},
  address = {{Cham}},
  doi = {10.1007/978-3-030-29333-8_4},
  urldate = {2024-01-12},
  abstract = {Relational event models are a powerful tool to examine how conflicts arise or manifest through human interactions and how they evolve over time. Building on event history analysis, these models combine network dependencies with temporal dynamics and allow for the analysis of group formation patterns{\textemdash}such as alliance or coalition formation processes{\textemdash}influencing dynamics or social learning. The added information on both the timing (and order) of social interactions as well as the context in which social interactions take place (i.e., the broader network in which people or actors are embedded in) can give powerful new evidence to theorized social mechanisms. This chapter provides an overview of REMs and showcases two empirical studies to illustrate the approach. The first study examines political alliance-formation patterns among countries engaging in military actions in the Gulf region. The REM shows that countries engage in military actions with other countries by balancing their relations, i.e., by supporting allies of their allies and opposing enemies of their allies. The second study shows that party family homophily guides parliamentary veto decisions and provides empirical evidence of social influencing dynamics among European parliaments.},
  isbn = {978-3-030-29333-8},
  langid = {english},
  keywords = {Conflict event data,Dynamic networks,Inferential network analysis,Social mechanisms,Temporal dependence},
  file = {/home/george/Zotero/storage/L5TTKUY3/Brandenberger - 2020 - Interdependencies in Conflict Dynamics Analyzing .pdf}
}

@article{krivitskyRejoinderDiscussionTale2023,
  title = {Rejoinder to {{Discussion}} of ``{{A Tale}} of {{Two Datasets}}: {{Representativeness}} and {{Generalisability}} of {{Inference}} for {{Samples}} of {{Networks}}''},
  shorttitle = {Rejoinder to {{Discussion}} of ``{{A Tale}} of {{Two Datasets}}},
  author = {Krivitsky, Pavel N. and Coletti, Pietro and Hens, Niel},
  year = {2023},
  month = oct,
  journal = {Journal of the American Statistical Association},
  volume = {118},
  number = {544},
  pages = {2235--2238},
  publisher = {{Taylor \& Francis}},
  issn = {0162-1459},
  doi = {10.1080/01621459.2023.2280383},
  urldate = {2024-01-12},
  file = {/home/george/Zotero/storage/RVHM362N/Krivitsky et al. - 2023 - Rejoinder to Discussion of “A Tale of Two Datasets.pdf}
}

@article{snijdersStochasticActorOriented1996,
  title = {Stochastic Actor-oriented Models for Network Change},
  author = {Snijders, Tom a B.},
  year = {1996},
  journal = {The Journal of Mathematical Sociology},
  volume = {21},
  number = {1-2},
  pages = {149--172},
  issn = {0022-250X},
  doi = {10.1080/0022250X.1996.9990178},
  abstract = {A class of models is proposed for longitudinal network data. These models are along the lines of methodological individualism: actors use heuristics to try to achieve their individual goals, subject to constraints. The current net- work structure is among these constraints. The models are continuous time Markov chain models that can be implemented as simulation models. They incorporate random change in addition to the purposeful change that follows from the actors' pursuit of their goals, and include parameters that must be estimated from observed data. Statistical methods are proposed for estimat- ing and testing these models. These methods can also be used for parameter estimation for other simulation models. The statistical procedures are based on the method of moments, and use computer simulation to estimate the theoretical moments. The Robbins-Monro process is used to deal with the stochastic nature of the estimated theoretical moments. An example is given for Newcomb's fraternity data, using a model that expresses reciprocity and balance.},
  isbn = {0022-250X},
  file = {/home/george/Zotero/storage/9MATIRIT/Snijders - 1996 - Stochastic actor‐oriented models for network chang.pdf}
}

@article{snijdersStochasticActorOrientedModels2017,
  title = {Stochastic {{Actor-Oriented Models}} for {{Network Dynamics}}},
  author = {Snijders, Tom A. B.},
  year = {2017},
  month = mar,
  journal = {Annual Review of Statistics and Its Application},
  volume = {4},
  number = {1},
  pages = {343--363},
  issn = {2326-8298},
  doi = {10.1146/annurev-statistics-060116-054035},
  file = {/home/george/Zotero/storage/2CFAB43P/Snijders - 2017 - Stochastic Actor-Oriented Models for Network Dynam.pdf}
}