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Journal articles on the topic 'Exponential Random Graph Models (ERGMs)'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Ghafouri, Saeid, and Seyed Hossein Khasteh. "A survey on exponential random graph models: an application perspective." PeerJ Computer Science 6 (April 6, 2020): e269. http://dx.doi.org/10.7717/peerj-cs.269.

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The uncertainty underlying real-world phenomena has attracted attention toward statistical analysis approaches. In this regard, many problems can be modeled as networks. Thus, the statistical analysis of networked problems has received special attention from many researchers in recent years. Exponential Random Graph Models, known as ERGMs, are one of the popular statistical methods for analyzing the graphs of networked data. ERGM is a generative statistical network model whose ultimate goal is to present a subset of networks with particular characteristics as a statistical distribution. In the

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2

Kim, Yeaji, Leonardo Antenangeli, and Justin Kirkland. "Measurement Error and Attenuation Bias in Exponential Random Graph Models." Statistics, Politics and Policy 7, no. 1-2 (2016): 29–54. http://dx.doi.org/10.1515/spp-2016-0001.

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AbstractExponential Random Graph Models (ERGMs) are becoming increasingly popular tools for estimating the properties of social networks across the social sciences. While the asymptotic properties of ERGMs are well understood, much less is known about how ERGMs perform in the face of violations of the assumptions that drive those asymptotic properties. Given that empirical social networks rarely meet the strenuous assumptions of the ERGM perfectly, practical researchers are often in the position of knowing their coefficients are imperfect, but not knowing precisely how wrong those coefficients

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Bekti, R. D., N. Pratiwi, Y. Niami, E. Sutanta, and E. K. Nurnawati. "Exponential Random Graph Models (ERGMs) to analyze the online shop networking in Instagram." Journal of Physics: Conference Series 1456 (January 2020): 012025. http://dx.doi.org/10.1088/1742-6596/1456/1/012025.

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Block, Per, Christoph Stadtfeld, and Tom A. B. Snijders. "Forms of Dependence: Comparing SAOMs and ERGMs From Basic Principles." Sociological Methods & Research 48, no. 1 (2016): 202–39. http://dx.doi.org/10.1177/0049124116672680.

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Two approaches for the statistical analysis of social network generation are widely used; the tie-oriented exponential random graph model (ERGM) and the stochastic actor-oriented model (SAOM) or Siena model. While the choice for either model by empirical researchers often seems arbitrary, there are important differences between these models that current literature tends to miss. First, the ERGM is defined on the graph level, while the SAOM is defined on the transition level. This allows the SAOM to model asymmetric or one-sided tie transition dependence. Second, network statistics in the ERGM

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Cranmer, Skyler J., and Bruce A. Desmarais. "Inferential Network Analysis with Exponential Random Graph Models." Political Analysis 19, no. 1 (2011): 66–86. http://dx.doi.org/10.1093/pan/mpq037.

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Methods for descriptive network analysis have reached statistical maturity and general acceptance across the social sciences in recent years. However, methods for statistical inference with network data remain fledgling by comparison. We introduce and evaluate a general model for inference with network data, the Exponential Random Graph Model (ERGM) and several of its recent extensions. The ERGM simultaneously allows both inference on covariates and for arbitrarily complex network structures to be modeled. Our contributions are three-fold: beyond introducing the ERGM and discussing its limitat

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Minhas, Shahryar, Peter D. Hoff, and Michael D. Ward. "Inferential Approaches for Network Analysis: AMEN for Latent Factor Models." Political Analysis 27, no. 2 (2018): 208–22. http://dx.doi.org/10.1017/pan.2018.50.

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We introduce a Bayesian approach to conduct inferential analyses on dyadic data while accounting for interdependencies between observations through a set of additive and multiplicative effects (AME). The AME model is built on a generalized linear modeling framework and is thus flexible enough to be applied to a variety of contexts. We contrast the AME model to two prominent approaches in the literature: the latent space model (LSM) and the exponential random graph model (ERGM). Relative to these approaches, we show that the AME approach is (a) to be easy to implement; (b) interpretable in a ge

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Pilny, Andrew, and Yannick Atouba. "Modeling Valued Organizational Communication Networks Using Exponential Random Graph Models." Management Communication Quarterly 32, no. 2 (2017): 250–64. http://dx.doi.org/10.1177/0893318917737179.

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For years, organizational communication scholars have been interested in the mechanisms that influence the formation of communication networks. One way to gain a deeper insight into the factors that shape such networks is to model them using exponential random graph modeling (ERGM). However, ERGM has only been applicable to binary networks, reducing communication to something that is either present or not. This article illustrates valued ERGM for organizational communication networks that have a weight associated with each tie. Using a data set on friendship strength between collaborative scie

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8

Cerqueira, Andressa, Aurélien Garivier, and Florencia Leonardi. "A note on perfect simulation for Exponential Random Graph Models." ESAIM: Probability and Statistics 24 (2020): 138–47. http://dx.doi.org/10.1051/ps/2019024.

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In this paper, we propose a perfect simulation algorithm for the Exponential Random Graph Model, based on the Coupling from the past method of Propp and Wilson (1996). We use a Glauber dynamics to construct the Markov Chain and we prove the monotonicity of the ERGM for a subset of the parametric space. We also obtain an upper bound on the running time of the algorithm that depends on the mixing time of the Markov chain.

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Chakraborty, Manajit, Maksym Byshkin, and Fabio Crestani. "Patent citation network analysis: A perspective from descriptive statistics and ERGMs." PLOS ONE 15, no. 12 (2020): e0241797. http://dx.doi.org/10.1371/journal.pone.0241797.

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Patent Citation Analysis has been gaining considerable traction over the past few decades. In this paper, we collect extensive information on patents and citations and provide a perspective of citation network analysis of patents from a statistical viewpoint. We identify and analyze the most cited patents, the most innovative and the highly cited companies along with the structural properties of the network by providing in-depth descriptive analysis. Furthermore, we employ Exponential Random Graph Models (ERGMs) to analyze the citation networks. ERGMs enables understanding the social perspecti

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10

Patterson, Megan S., Katie M. Heinrich, Tyler Prochnow, Taylor Graves-Boswell, and Mandy N. Spadine. "Network Analysis of the Social Environment Relative to Preference for and Tolerance of Exercise Intensity in CrossFit Gyms." International Journal of Environmental Research and Public Health 17, no. 22 (2020): 8370. http://dx.doi.org/10.3390/ijerph17228370.

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Known for its ability to improve fitness and health, high-intensity functional training (HIFT) focuses on functional movements completed at high intensities, often yielding outcomes superior to repetitive aerobic workouts. Preference for and tolerance of high-intensity exercise are associated with enjoyment of and adherence to HIFT. Similarly, the social environment present within CrossFit, a popular group-based HIFT modality, is important to the enjoyment of and adherence to HIFT. This study aimed to test whether preference and tolerance were related to social connections within CrossFit netw

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Obando, Catalina, and Fabrizio De Vico Fallani. "A statistical model for brain networks inferred from large-scale electrophysiological signals." Journal of The Royal Society Interface 14, no. 128 (2017): 20160940. http://dx.doi.org/10.1098/rsif.2016.0940.

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Network science has been extensively developed to characterize the structural properties of complex systems, including brain networks inferred from neuroimaging data. As a result of the inference process, networks estimated from experimentally obtained biological data represent one instance of a larger number of realizations with similar intrinsic topology. A modelling approach is therefore needed to support statistical inference on the bottom-up local connectivity mechanisms influencing the formation of the estimated brain networks. Here, we adopted a statistical model based on exponential ra

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Goeyvaerts, Nele, Eva Santermans, Gail Potter, et al. "Household members do not contact each other at random: implications for infectious disease modelling." Proceedings of the Royal Society B: Biological Sciences 285, no. 1893 (2018): 20182201. http://dx.doi.org/10.1098/rspb.2018.2201.

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Airborne infectious diseases such as influenza are primarily transmitted from human to human by means of social contacts, and thus easily spread within households. Epidemic models, used to gain insight into infectious disease spread and control, typically rely on the assumption of random mixing within households. Until now, there has been no direct empirical evidence to support this assumption. Here, we present the first social contact survey specifically designed to study contact networks within households. The survey was conducted in Belgium (Flanders and Brussels) from 2010 to 2011. We anal

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13

Yang, Donghui, Chao Huang, and Mingyang Wang. "A social recommender system by combining social network and sentiment similarity: A case study of healthcare." Journal of Information Science 43, no. 5 (2016): 635–48. http://dx.doi.org/10.1177/0165551516657712.

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Social recommender systems aim to support user preferences and help users make better decisions in social media. The social network and the social context are two vital elements in social recommender systems. In this contribution, we propose a new framework for a social recommender system based on both network structure analysis and social context mining. Exponential random graph models (ERGMs) are able to capture and simulate the complex structure of a micro-blog network. We derive the prediction formula from ERGMs for recommending micro-blog users. Then, a primary recommendation list is crea

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14

Kreager, Derek A., Jacob T. N. Young, Dana L. Haynie, Martin Bouchard, David R. Schaefer, and Gary Zajac. "Where “Old Heads” Prevail: Inmate Hierarchy in a Men’s Prison Unit." American Sociological Review 82, no. 4 (2017): 685–718. http://dx.doi.org/10.1177/0003122417710462.

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Research on inmate social order, a once-vibrant area, receded just as U.S. incarceration rates climbed and the country’s carceral contexts dramatically changed. This study returns to inmate society with an abductive mixed-methods investigation of informal status within a contemporary men’s prison unit. We collected narrative and social network data from 133 male inmates housed in a unit of a Pennsylvania medium-security prison. Analyses of inmate narratives suggest that unit “old heads” provide collective goods in the form of mentoring and role modeling that foster a positive and stable peer e

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15

Box-Steffensmeier, Janet M., Dino P. Christenson, and Jason W. Morgan. "Modeling Unobserved Heterogeneity in Social Networks with the Frailty Exponential Random Graph Model." Political Analysis 26, no. 1 (2018): 3–19. http://dx.doi.org/10.1017/pan.2017.23.

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In the study of social processes, the presence of unobserved heterogeneity is a regular concern. It should be particularly worrisome for the statistical analysis of networks, given the complex dependencies that shape network formation combined with the restrictive assumptions of related models. In this paper, we demonstrate the importance of explicitly accounting for unobserved heterogeneity in exponential random graph models (ERGM) with a Monte Carlo analysis and two applications that have played an important role in the networks literature. Overall, these analyses show that failing to accoun

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Tao, Zhigang, and Haibo Zhang. "Partnering Strategies of Organizational Networks in Complex Environment of Disaster in the Centralized Political Context." Complexity 2020 (December 1, 2020): 1–13. http://dx.doi.org/10.1155/2020/9687390.

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Organizational networks are a widely used approach to deal with the “wicked problems” of disasters. However, current studies are insufficient in examining what strategies organizations actually employ to select partners in a complex environment of disaster, particularly in the centralized administrative context. This case study uses exponential random graph models (ERGMs) to explore different partnering strategies that organizations used to form organizational networks in response to the Tianjin Port blast, a well-known disaster in China. Results demonstrate that participating organizations pr

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17

Palacios, Diego, and Cristóbal Villalobos. "Academic networks within Chilean schools: An exploratory study using Exponential Random Graph Models (ERGM)." Redes. Revista hispana para el análisis de redes sociales 27, no. 2 (2016): 33. http://dx.doi.org/10.5565/rev/redes.631.

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18

Olivier, Tomás. "How Do Institutions Address Collective-Action Problems? Bridging and Bonding in Institutional Design." Political Research Quarterly 72, no. 1 (2018): 162–76. http://dx.doi.org/10.1177/1065912918784199.

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Collective-action problems affect the structure of stakeholder networks differently in policy settings (Berardo and Scholz 2010). However, interactions in policy settings do not usually occur in an institutional vacuum; instead, they are guided and constrained by agreed-on rules. Therefore, to better understand behavior in these settings, it is important to understand the parameters that guide and constrain it. Combining arguments from game theory and social network analysis, this paper focuses on how the nature of collective-action problems affect the design of formal institutional arrangemen

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19

van der Pol, Johannes. "Introduction to Network Modeling Using Exponential Random Graph Models (ERGM): Theory and an Application Using R-Project." Computational Economics 54, no. 3 (2018): 845–75. http://dx.doi.org/10.1007/s10614-018-9853-2.

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20

AN, WEIHUA, and WILL R. MCCONNELL. "The origins of asymmetric ties in friendship networks: From status differential to self-perceived centrality." Network Science 3, no. 2 (2015): 269–92. http://dx.doi.org/10.1017/nws.2015.12.

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AbstractAsymmetric ties make up a significant proportion of ties in friendship networks. But little is known about their origins. Prior research has suggested treating them either as “accidental” (e.g., resulting from constraints in name generators) or “aspirational” (i.e., the attempts of individuals to pursue relationships with higher status peers). In this paper, we show that self-perception can also explain the occurrence of asymmetric ties. We argue that under the general norm of reciprocity, actors with high self-perceived centrality will more likely send out ties to others than their co

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21

Cao, Qiuchang, Li Liao, and Keith Leverett Warren. "Social and programmatic interactions in a therapeutic community for women: an exponential random graph model analysis." Therapeutic Communities: The International Journal of Therapeutic Communities 41, no. 3/4 (2020): 69–79. http://dx.doi.org/10.1108/tc-08-2019-0008.

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Purpose To analyze networks of social interactions between the residents of a therapeutic community (TC) for women and the way, in which such interactions predict the discussion of issues that arise in treatment. Design/methodology/approach In total, 50 residents of a corrections-based TC for women were surveyed on the peers with whom they socialized informally, shared meals, shared letters from home and discussed issues that arose in treatment over a 12 h period. The data were analyzed using exponential random graph models (ERGM). Findings Reciprocity occurred in all networks while transitivi

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22

Wickramasinghe, Ashani Nuwanthika, and Saman Muthukumarana. "Social network analysis and community detection on spread of COVID-19." Model Assisted Statistics and Applications 16, no. 1 (2021): 37–52. http://dx.doi.org/10.3233/mas-210513.

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This paper explains the epidemic spread using social network analysis, based on data from the first three months of the 2020 COVID-19 outbreak across the world and in Canada. A network is defined and visualization is used to understand the spread of coronavirus among countries and the impact of other countries on the spread of coronavirus in Canada. The degree centrality is used to identify the main influencing countries. Exponential Random Graph Models (ERGM) are used to identify the processes that influence link creation between countries. The community detection is done using Infomap, Label

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23

Sha, Zhenghui, Youyi Bi, Mingxian Wang, et al. "Comparing Utility-based and Network-based Approaches in Modeling Customer Preferences for Engineering Design." Proceedings of the Design Society: International Conference on Engineering Design 1, no. 1 (2019): 3831–40. http://dx.doi.org/10.1017/dsi.2019.390.

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AbstractCustomer preference modeling provides quantitative assessment of the effects of engineering design attributes on customers’ choices. Utility-based approaches, such as discrete choice model (DCM), and network analysis approaches, such as exponential random graph model (ERGM), have been developed for customer preference modeling. However, no studies have compared these two approaches. Our objective is to identify the distinctions and connections between these two approaches based on both the theoretical foundation and the empirical evidence. Using the vehicle preference modeling as an ex

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24

Baggio, Stéphanie, Victorin Luisier, and Cristina Vladescu. "Relationships Between Social Networks and Mental Health." Swiss Journal of Psychology 76, no. 1 (2017): 5–11. http://dx.doi.org/10.1024/1421-0185/a000186.

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Abstract. Social networks have an important effect on health, and social network analysis has become essential for understanding human behavior and vulnerability. Using exponential random graph models (ERGM), this study explores the associations between mental health and network structure (or more specifically, mental health homophily) and the association between poor mental health and social isolation. Two classes of Romanian adolescents aged 12–14 years participated in the study (n = 26 in each class). We assessed school network, sociodemographic covariates, and mental health using the Stren

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25

Kacanski, Slobodan. "Structure behind principles: social selection mechanisms in corporate governance networks." Corporate Governance: The International Journal of Business in Society 20, no. 1 (2019): 87–105. http://dx.doi.org/10.1108/cg-02-2019-0063.

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Purpose The purpose of this study is to show that social relations in a corporate governance platform between members of supervisory boards and between members of supervisory and executive board tiers can serve as an alternative viewpoint for understanding mechanisms of social selection in corporate governance networks. The study shows that through the lenses of social network analysis, it is possible to identify and understand how the process of corporate governance member selection unfolds within companies and how that selection process may have been potentially influenced by the cross-board

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Sun, Keke, Xia Cao, and Zeyu Xing. "Can the Diffusion Modes of Green Technology Affect the Enterprise’s Technology Diffusion Network towards Sustainable Development of Hospitality and Tourism Industry in China?" Sustainability 13, no. 16 (2021): 9266. http://dx.doi.org/10.3390/su13169266.

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In the post-epidemic era, encouraging enterprises to implement green technology innovation in the hospitality and tourism industry is important, which can reduce resource consumption, decrease environmental pollution and promote sustainable industrial development. Based on evolutionary game theory and Exponential Random Graph Models (ERGM), this paper develops an evolutionary game model between focal and marginal enterprises and analyzes the dynamic evolutionary process and the steady state of the evolutionary strategy of the major stakeholders. The impact of different technology diffusion mod

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Vasconcelos, Geraldo Magela Rodrigues De, Gustavo Melo-Silva, and Velcimiro Inácio Maia. "Social network analysis of lodging establishments in Tiradentes (MG): Profile and evidence of relationships genesis." Turismo - Visão e Ação 22, no. 3 (2020): 424–45. http://dx.doi.org/10.14210/rtva.v22n3.p424-445.

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A análise de redes sociais (ARS) constitui um grande avanço na pesquisa em turismo ao revelar as características das relações estabelecidas, apresentando suas estruturas e propriedades. Este trabalho objetivou caracterizar e analisar a rede de cooperação formada entre proprietários de pousadas em Tiradentes-MG. Com o objetivo de explorar o contexto da pesquisa, foram realizadas entrevistas junto a sete proprietários de pousadas. Posteriormente, para a coleta dos dados, foi aplicado um questionário aos proprietários. Por meio da técnica da “bola de neve” foi gerada a rede de cooperação. A parti

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28

Suesse, Thomas. "Marginalized Exponential Random Graph Models." Journal of Computational and Graphical Statistics 21, no. 4 (2012): 883–900. http://dx.doi.org/10.1080/10618600.2012.694750.

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29

Yu, Yue, Gianmarc Grazioli, Nolan E. Phillips, and Carter T. Butts. "Local Graph Stability in Exponential Family Random Graph Models." SIAM Journal on Applied Mathematics 81, no. 4 (2021): 1389–415. http://dx.doi.org/10.1137/19m1286864.

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30

Robins, Garry, and Martina Morris. "Advances in exponential random graph (p*) models." Social Networks 29, no. 2 (2007): 169–72. http://dx.doi.org/10.1016/j.socnet.2006.08.004.

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Wang, Peng, Ken Sharpe, Garry L. Robins, and Philippa E. Pattison. "Exponential random graph () models for affiliation networks." Social Networks 31, no. 1 (2009): 12–25. http://dx.doi.org/10.1016/j.socnet.2008.08.002.

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Caimo, Alberto, and Nial Friel. "Bayesian inference for exponential random graph models." Social Networks 33, no. 1 (2011): 41–55. http://dx.doi.org/10.1016/j.socnet.2010.09.004.

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Wang, Peng, Garry Robins, Philippa Pattison, and Emmanuel Lazega. "Exponential random graph models for multilevel networks." Social Networks 35, no. 1 (2013): 96–115. http://dx.doi.org/10.1016/j.socnet.2013.01.004.

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Vega Yon, George G., Andrew Slaughter, and Kayla de la Haye. "Exponential random graph models for little networks." Social Networks 64 (January 2021): 225–38. http://dx.doi.org/10.1016/j.socnet.2020.07.005.

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Brailly, Julien, and Scott Viallet-Thevenin. "Exponential Random Graph Models for Social Networks." Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique 123, no. 1 (2014): 80–87. http://dx.doi.org/10.1177/0759106314534362.

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Chatterjee, Sourav, and Persi Diaconis. "Estimating and understanding exponential random graph models." Annals of Statistics 41, no. 5 (2013): 2428–61. http://dx.doi.org/10.1214/13-aos1155.

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Yin, Mei, and Lingjiong Zhu. "Asymptotics for sparse exponential random graph models." Brazilian Journal of Probability and Statistics 31, no. 2 (2017): 394–412. http://dx.doi.org/10.1214/16-bjps319.

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Snijders, Tom A. B., Philippa E. Pattison, Garry L. Robins, and Mark S. Handcock. "New Specifications for Exponential Random Graph Models." Sociological Methodology 36, no. 1 (2006): 99–153. http://dx.doi.org/10.1111/j.1467-9531.2006.00176.x.

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SNIJDERS, TOM A. B. "Conditional Marginalization for Exponential Random Graph Models." Journal of Mathematical Sociology 34, no. 4 (2010): 239–52. http://dx.doi.org/10.1080/0022250x.2010.485707.

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Thiemichen, S., N. Friel, A. Caimo, and G. Kauermann. "Bayesian exponential random graph models with nodal random effects." Social Networks 46 (July 2016): 11–28. http://dx.doi.org/10.1016/j.socnet.2016.01.002.

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Draief, M., A. Ganesh, and L. Massoulié. "Exponential Random Graphs as Models of Overlay Networks." Journal of Applied Probability 46, no. 01 (2009): 199–220. http://dx.doi.org/10.1017/s0021900200005313.

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In this paper we give an analytic solution for graphs with n nodes and E = cn log n edges for which the probability of obtaining a given graph G is µn (G) = exp (- β ∑i=1 n d i 2), where d i is the degree of node i. We describe how this model appears in the context of load balancing in communication networks, namely peer-to-peer overlays. We then analyse the degree distribution of such graphs and show that the degrees are concentrated around their mean value. Finally, we derive asymptotic results for the number of edges crossing a graph cut and use these results (i) to compute the graph expans

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Caimo, A., and N. Friel. "Bayesian model selection for exponential random graph models." Social Networks 35, no. 1 (2013): 11–24. http://dx.doi.org/10.1016/j.socnet.2012.10.003.

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Daniel, João R., António J. Santos, Inês Peceguina, and Brian E. Vaughn. "Exponential random graph models of preschool affiliative networks." Social Networks 35, no. 1 (2013): 25–30. http://dx.doi.org/10.1016/j.socnet.2012.11.002.

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Brughmans, Tom, Simon Keay, and Graeme Earl. "Introducing exponential random graph models for visibility networks." Journal of Archaeological Science 49 (September 2014): 442–54. http://dx.doi.org/10.1016/j.jas.2014.05.027.

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Tan, Linda S. L., and Nial Friel. "Bayesian Variational Inference for Exponential Random Graph Models." Journal of Computational and Graphical Statistics 29, no. 4 (2020): 910–28. http://dx.doi.org/10.1080/10618600.2020.1740714.

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Shalizi, Cosma Rohilla, and Alessandro Rinaldo. "Consistency under sampling of exponential random graph models." Annals of Statistics 41, no. 2 (2013): 508–35. http://dx.doi.org/10.1214/12-aos1044.

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47

Krivitsky, Pavel N. "Exponential-family random graph models for valued networks." Electronic Journal of Statistics 6 (2012): 1100–1128. http://dx.doi.org/10.1214/12-ejs696.

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Byshkin, Maksym, Alex Stivala, Antonietta Mira, Rolf Krause, Garry Robins, and Alessandro Lomi. "Auxiliary Parameter MCMC for Exponential Random Graph Models." Journal of Statistical Physics 165, no. 4 (2016): 740–54. http://dx.doi.org/10.1007/s10955-016-1650-5.

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Zhu, Lingjiong. "Asymptotic Structure of Constrained Exponential Random Graph Models." Journal of Statistical Physics 166, no. 6 (2017): 1464–82. http://dx.doi.org/10.1007/s10955-017-1733-y.

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Draief, M., A. Ganesh, and L. Massoulié. "Exponential Random Graphs as Models of Overlay Networks." Journal of Applied Probability 46, no. 1 (2009): 199–220. http://dx.doi.org/10.1239/jap/1238592125.

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In this paper we give an analytic solution for graphs withnnodes andE=cnlognedges for which the probability of obtaining a given graphGisµn(G) = exp (-β∑i=1ndi2), wherediis the degree of nodei. We describe how this model appears in the context of load balancing in communication networks, namely peer-to-peer overlays. We then analyse the degree distribution of such graphs and show that the degrees are concentrated around their mean value. Finally, we derive asymptotic results for the number of edges crossing a graph cut and use these results (i) to compute the graph expansion and conductance, a

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