Relevant bibliographies by topics / Exponential Random Graph Models (ERGMs)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Exponential Random Graph Models (ERGMs)'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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

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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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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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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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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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Dissertations / Theses on the topic "Exponential Random Graph Models (ERGMs)"

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Cimenler, Oguz. "Social Network Analysis of Researchers' Communication and Collaborative Networks Using Self-reported Data." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5201.

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This research seeks an answer to the following question: what is the relationship between the structure of researchers' communication network and the structure of their collaborative output networks (e.g. co-authored publications, joint grant proposals, and joint patent applications), and the impact of these structures on their citation performance and the volume of collaborative research outputs? Three complementary studies are performed to answer this main question as discussed below. 1. Study I: A frequently used output to measure scientific (or research) collaboration is co-authorship in s
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Laporte, Quentin. "Étude morpho-statistique des réseaux sociaux. Application aux collaborations inter-organisationnelles." Electronic Thesis or Diss., Université de Lorraine, 2021. http://www.theses.fr/2021LORR0007.

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Les applications collaboratives décentralisées permettent de répondre aux problèmes de confidentialité, de disponibilité et de sécurité inhérents aux plateformes collaboratives centralisées. Elles reposent sur un paradigme de communication pair-à-pair selon lequel tous les utilisateurs sont directement connectés les uns aux autres. Les collaborations ayant tendance à s'élargir et dépasser les frontières des organisations, il est nécessaire de garantir aux utilisateurs le contrôle sur leurs données tout en assurant la disponibilité de la collaboration. Pour ce faire, il est possible d'utiliser
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Duxbury, Scott W. "Diagnosing Multicollinearity in Exponential Random Graph Models." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1491393848069144.

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Jiang, Shan. "Statistical Modeling of Multi-Dimensional Knowledge Diffusion Networks: An ERGM-Based Framework." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/555946.

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Knowledge diffusion networks consist of individuals who exchange knowledge and knowledge flows connecting the individuals. By studying knowledge diffusion in a network perspective, it helps us understand how the connections between individuals affect the knowledge diffusion processes. Existing research on knowledge diffusion networks mostly adopts a uni-dimensional perspective, where all the individuals in the networks are assumed to be of the same type. It also assumes that there is only one type of knowledge flow in the network. This dissertation proposes a multi-dimensional perspective of k
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Thiemichen, Stephanie [Verfasser], and Göran [Akademischer Betreuer] Kauermann. "Extensions of exponential random graph models for network data analysis / Stephanie Thiemichen. Betreuer: Göran Kauermann." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2016. http://d-nb.info/1108130119/34.

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Willardson, Spencer L. "Under the influence Of arms: the foreign policy causes and consequences of arms transfers." Diss., University of Iowa, 2013. https://ir.uiowa.edu/etd/2660.

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How are arms export choices made within a state? In this dissertation I use a foreign policy analysis framework to examine this question. I focus on examining each of the three primary levels of analysis in international relations as it relates to the main question. I begin with a typical international relations level and examine the characteristics of the two states that dominate the world arms trade: The United States and Russia. I then examine the full network of relations among all states in the international system th
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Pitts, Christine Maria. "A Dynamic Network Study on How Consolidating State Governance Models Relates to Legislator Voting Patterns." Thesis, University of Oregon, 2018. http://hdl.handle.net/1794/23766.

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In 2011, Oregon was one of many states in the U.S. consolidating their education governance around an early learning, K-12, and postsecondary hub. This study uses legislator-voting data to investigate the relationship between this consolidated model and endogenous policy formulation processes. This study employs a separable temporal exponential random graph model (STERGM) to investigate how an education governance shift toward consolidated authority relates to bipartisan outcomes for education-related bills over time. Oregon legislator voting networks were analyzed for cohesion, centrality, an
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Song, Hyunjin. "A Dynamic Longitudinal Examination of Social Networks and Political Behavior: The Moderating Effect of Local Network Properties and Its Implication for Social Influence Processes." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1427490761.

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Hou, Meng-Hsiu, and 侯孟秀. "Comparing Social Network Exchange Patterns in Different Types of Virtual Communities : An Exponential Random Graph Models (ERGMs) Approach." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/b74w4d.

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碩士<br>國立臺灣大學<br>圖書資訊學研究所<br>106<br>Virtual communities have become an important venue for individuals to seek information and find social support. Previous studies indicate that different types of virtual communities have distinct interaction patterns among members, it is therefore inappropriate to treat all virtual communities as the same because of their unique network structures. Specifically, three types of virtual communities have been distinguished: “question and answer”, “discussion” and ” social support”. To examine whether different virtual communities do indeed have different network
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Sun, Wei-Chen, and 孫維楨. "Social Contexts of Gender Composition for College Students, Gender and Mechanisms of Friendship Networks: Exploration of Exponential Random Graph Models (ERGMs)." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/m73umw.

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碩士<br>國立政治大學<br>社會學系<br>106<br>The impact of different social contexts on individual behavior is an important issue that sociology has always been concerned about. In the early time, Peter Blau (1974) had already emphasized that different social contexts are the causes of different interpersonal interactions. Ridgeway (2006) also emphasized that individual behavior varies with different expectations of the positions in society. Among one of them, Gender is a key issue which has been discussed since the beginning of sociology. Since the birth of a new life, even it hasn’t started to breath, gen
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Books on the topic "Exponential Random Graph Models (ERGMs)"

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Lusher, Dean, Johan Koskinen, and Garry Robins, eds. Exponential Random Graph Models for Social Networks. Cambridge University Press, 2012. http://dx.doi.org/10.1017/cbo9780511894701.

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Exponential random graph models for social networks: Theories, methods, and applications. Cambridge University Press, 2012.

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Coolen, A. C. C., A. Annibale, and E. S. Roberts. Soft constraints: exponential random graph models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0004.

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Exponential random graph models (ERGMs) provide conceptually elegant recipes for generating soft-constrained random graphs. This chapter begins by explaining the theory and describing how to properly specify an ERGM, including demonstrating Lagrange’s method to derive the values of the model parameters that correspond to the desired constraints. Three ERGMs, all with constraints depending linearly on the adjacency matrix, are solved exactly: the targeted total number of links, targeted individual node degrees and targeted number of two-way links in a directed graph. However, when the controlle
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Coolen, Ton, Alessia Annibale, and Ekaterina Roberts. Generating Random Networks and Graphs. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.001.0001.

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This book supports researchers who need to generate random networks, or who are interested in the theoretical study of random graphs. The coverage includes exponential random graphs (where the targeted probability of each network appearing in the ensemble is specified), growth algorithms (i.e. preferential attachment and the stub-joining configuration model), special constructions (e.g. geometric graphs and Watts Strogatz models) and graphs on structured spaces (e.g. multiplex networks). The presentation aims to be a complete starting point, including details of both theory and implementation,
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Desmarais, Bruce A., and Skyler J. Cranmer. Statistical Inference in Political Networks Research. Edited by Jennifer Nicoll Victor, Alexander H. Montgomery, and Mark Lubell. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.8.

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Researchers interested in statistically modeling network data have a well-established and quickly growing set of approaches from which to choose. Several of these methods have been regularly applied in research on political networks, while others have yet to permeate the field. This chapter reviews the most prominent methods of inferential network analysis for both cross-sectionally and longitudinally observed networks, including (temporal) exponential random graph models, latent space models, the quadratic assignment procedure, and stochastic actor oriented models. For each method, the chapte
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Coolen, A. C. C., A. Annibale, and E. S. Roberts. Graphs with hard constraints: further applications and extensions. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0007.

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This chapter looks at further topics pertaining to the effective use of Markov Chain Monte Carlo to sample from hard- and soft-constrained exponential random graph models. The chapter considers the question of how moves can be sampled efficiently without introducing unintended bias. It is shown mathematically and numerically that apparently very similar methods of picking out moves can give rise to significant differences in the average topology of the networks generated by the MCMC process. The general discussion in complemented with pseudocode in the relevant section of the Algorithms chapte
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Book chapters on the topic "Exponential Random Graph Models (ERGMs)"

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Fronczak, Agata. "Exponential Random Graph Models." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_233.

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Fronczak, Agata. "Exponential Random Graph Models." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_233.

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Chatterjee, Sourav. "Exponential Random Graph Models." In Lecture Notes in Mathematics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65816-2_7.

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Fronczak, Agata. "Exponential Random Graph Models." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4614-7163-9_233-1.

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Robins, Garry. "Social Networks, Exponential Random Graph (p *) Models for." In Computational Complexity. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1800-9_182.

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Robins, Garry. "Social Networks, Exponential Random Graph (p *) Models for." In Encyclopedia of Complexity and Systems Science. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-30440-3_494.

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Krause, Robert W., and Alberto Caimo. "Missing Data Augmentation for Bayesian Exponential Random Multi-Graph Models." In Complex Networks X. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14459-3_5.

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Zaccarin, Susanna, and Giulia Rivellini. "Modelling Network Data: An Introduction to Exponential Random Graph Models." In Data Analysis and Classification. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03739-9_34.

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Kuskova, Valentina, Gregory Khvatsky, Dmitry Zaytsev, and Nikita Talovsky. "Multilevel Exponential Random Graph Models Application to Civil Participation Studies." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37334-4_24.

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Broekel, Tom, and Matté Hartog. "Determinants of Cross-Regional R&D Collaboration Networks: An Application of Exponential Random Graph Models." In Advances in Spatial Science. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02699-2_4.

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Conference papers on the topic "Exponential Random Graph Models (ERGMs)"

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Xie, Jian, Youyi Bi, Zhenghui Sha, et al. "Data-Driven Dynamic Network Modeling for Analyzing the Evolution of Product Competitions." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-98385.

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Abstract Understanding the impact of engineering design on product competitions is imperative for product designers to better address customer needs and develop more competitive products. In this paper, we propose a dynamic network based approach to modeling and analyzing the evolution of product competitions using multi-year product survey data. We adopt Separate Temporal Exponential Random Graph Model (STERGM) as the statistical inference framework because it considers the evolution of dynamic networks as two separate processes: formation and dissolution. This treatment allows designers to i
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Tripathi, Shubham, and Grethe Hystad. "EXPONENTIAL RANDOM GRAPH MODELS FOR MINERAL NETWORK DATA." In GSA Annual Meeting in Phoenix, Arizona, USA - 2019. Geological Society of America, 2019. http://dx.doi.org/10.1130/abs/2019am-334822.

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Shang, Yilun. "Exponential random geometric graph process models for mobile wireless networks." In 2009 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). IEEE, 2009. http://dx.doi.org/10.1109/cyberc.2009.5342212.

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He, Ran, and Tian Zheng. "Estimation of exponential random graph models for large social networks via graph limits." In ASONAM '13: Advances in Social Networks Analysis and Mining 2013. ACM, 2013. http://dx.doi.org/10.1145/2492517.2492523.

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He, Ran, and Tian Zheng. "Estimating exponential random graph models using sampled network data via graphon." In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2016. http://dx.doi.org/10.1109/asonam.2016.7752222.

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Liu, Fang, Evercita Eugenio, Ick Hoon Jin, and Claire Bowen. "Differentially Private Generation of Social Networks via Exponential Random Graph Models." In 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2020. http://dx.doi.org/10.1109/compsac48688.2020.00-11.

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Feng, Pihu, Duoyong Sun, and Zhan Feng Li. "Research on Pyramid Schemes Communication Network Based on Exponential Random Graph Models." In 2019 IEEE 4th International Conference on Big Data Analytics (ICBDA). IEEE, 2019. http://dx.doi.org/10.1109/icbda.2019.8713241.

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Sha, Zhenghui, Ashish M. Chaudhari, and Jitesh H. Panchal. "Modeling Participation Behaviors in Design Crowdsourcing Using a Bipartite Network-Based Approach." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85686.

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This paper studies the participation behaviors in design crowdsourcing by modeling associations between participants and design contests as a bipartite network. Such a network consists of two types of nodes, participant nodes and design contest nodes, and the links indicating participating relations. Our hypothesis is that participants’ decisions are interdependent. For example, one participant’s decision on whether to participate in a contest depends on whether other participants have participated in the same contest or not. To test the hypothesis, the exponential random graph model (ERGM) is
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Schmid, Christian S., and Bruce A. Desmarais. "Exponential random graph models with big networks: Maximum pseudolikelihood estimation and the parametric bootstrap." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8257919.

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Li, Zhouzhi, and Wei Kang. "Research on the Cooperation of Emergency Response Based on the Exponential Random Graph Models." In 2016 International Seminar on Education Innovation and Economic Management (SEIEM 2016). Atlantis Press, 2016. http://dx.doi.org/10.2991/seiem-16.2016.107.

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