Manuel Muñoz-Herrera, J. Dijkstra, A. Flache, R. Wittek
Abstract We develop a model of strategic network formation of collaborations to analyze the consequences of an understudied but consequential form of heterogeneity: differences between actors in the form of their production functions. We also address how this interacts with resource heterogeneity, as a way to measure the impact actors have as potential partners on a collaborative project. Some actors (e.g., start-up firms) may exhibit increasing returns to their investment into collaboration projects, while others (e.g., established firms) may face decreasing returns. Our model provides insights into how actor heterogeneity can help explain well-observed collaboration patterns. We show that if there is a direct relation between increasing returns and resources, start-ups exclude mature firms and networks become segregated by types of production function, portraying dominant group architectures. On the other hand, if there is an inverse relation between increasing returns and resources, networks portray core-periphery architectures, where the mature firms form a core and start-ups with low-resources link to them.
{"title":"Collaborative production networks among unequal actors","authors":"Manuel Muñoz-Herrera, J. Dijkstra, A. Flache, R. Wittek","doi":"10.1017/nws.2020.23","DOIUrl":"https://doi.org/10.1017/nws.2020.23","url":null,"abstract":"Abstract We develop a model of strategic network formation of collaborations to analyze the consequences of an understudied but consequential form of heterogeneity: differences between actors in the form of their production functions. We also address how this interacts with resource heterogeneity, as a way to measure the impact actors have as potential partners on a collaborative project. Some actors (e.g., start-up firms) may exhibit increasing returns to their investment into collaboration projects, while others (e.g., established firms) may face decreasing returns. Our model provides insights into how actor heterogeneity can help explain well-observed collaboration patterns. We show that if there is a direct relation between increasing returns and resources, start-ups exclude mature firms and networks become segregated by types of production function, portraying dominant group architectures. On the other hand, if there is an inverse relation between increasing returns and resources, networks portray core-periphery architectures, where the mature firms form a core and start-ups with low-resources link to them.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":"9 1","pages":"1 - 17"},"PeriodicalIF":1.7,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.23","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43560415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We are extremely pleased to present this special issue of Network Science which contains a collec-tion of extended papers from the Seventh International Conference on Complex Networks & their Applications (COMPLEX NETWORKS 2018). Initiated in 2011, the conference series has grown to become one of the major international events in network science. Every year, it brings together researchers from a wide variety of scientific backgrounds ranging from finance and economics, medicine and neuroscience, biology and earth sciences, sociology and political science, computer science, physics, and many others in order to review the current state of the field and formu-late new directions. The great diversity of the participants allows for cross-fertilization between fundamental issues and innovative applications.
{"title":"Introduction to the special issue on COMPLEX NETWORKS 2018","authors":"H. Cherifi, Luis Mateus Rocha, S. Wasserman","doi":"10.1017/nws.2020.22","DOIUrl":"https://doi.org/10.1017/nws.2020.22","url":null,"abstract":"We are extremely pleased to present this special issue of Network Science which contains a collec-tion of extended papers from the Seventh International Conference on Complex Networks & their Applications (COMPLEX NETWORKS 2018). Initiated in 2011, the conference series has grown to become one of the major international events in network science. Every year, it brings together researchers from a wide variety of scientific backgrounds ranging from finance and economics, medicine and neuroscience, biology and earth sciences, sociology and political science, computer science, physics, and many others in order to review the current state of the field and formu-late new directions. The great diversity of the participants allows for cross-fertilization between fundamental issues and innovative applications.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":"8 1","pages":"S1 - S3"},"PeriodicalIF":1.7,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.22","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47960905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Latent position network models are a versatile tool in network science; applications include clustering entities, controlling for causal confounders, and defining priors over unobserved graphs. Estimating each node’s latent position is typically framed as a Bayesian inference problem, with Metropolis within Gibbs being the most popular tool for approximating the posterior distribution. However, it is well-known that Metropolis within Gibbs is inefficient for large networks; the acceptance ratios are expensive to compute, and the resultant posterior draws are highly correlated. In this article, we propose an alternative Markov chain Monte Carlo strategy—defined using a combination of split Hamiltonian Monte Carlo and Firefly Monte Carlo—that leverages the posterior distribution’s functional form for more efficient posterior computation. We demonstrate that these strategies outperform Metropolis within Gibbs and other algorithms on synthetic networks, as well as on real information-sharing networks of teachers and staff in a school district.
{"title":"Faster MCMC for Gaussian latent position network models","authors":"Neil A. Spencer, B. Junker, T. Sweet","doi":"10.1017/nws.2022.1","DOIUrl":"https://doi.org/10.1017/nws.2022.1","url":null,"abstract":"Abstract Latent position network models are a versatile tool in network science; applications include clustering entities, controlling for causal confounders, and defining priors over unobserved graphs. Estimating each node’s latent position is typically framed as a Bayesian inference problem, with Metropolis within Gibbs being the most popular tool for approximating the posterior distribution. However, it is well-known that Metropolis within Gibbs is inefficient for large networks; the acceptance ratios are expensive to compute, and the resultant posterior draws are highly correlated. In this article, we propose an alternative Markov chain Monte Carlo strategy—defined using a combination of split Hamiltonian Monte Carlo and Firefly Monte Carlo—that leverages the posterior distribution’s functional form for more efficient posterior computation. We demonstrate that these strategies outperform Metropolis within Gibbs and other algorithms on synthetic networks, as well as on real information-sharing networks of teachers and staff in a school district.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":"10 1","pages":"20 - 45"},"PeriodicalIF":1.7,"publicationDate":"2020-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46808856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
1Department of Sociology and Indiana University Network Science Institute, Indiana University Bloomington, Bloomington IN, 47408, USA (e-mail: blperry@indiana.edu), 2Department of Sociology, Indiana Consortium for Mental Health Services, Indiana University Network Science Institute, Indiana University Bloomington, Bloomington IN, 47408, USA (e-mail: pescosol@indiana.edu), 3Department of Sociology, Harvard University, Cambridge, MA 02138, USA (e-mail: mariosmall@fas.harvard.edu), 4Indiana University Network Science Institute, Indiana University Bloomington, Bloomington IN, 47408, USA ∗Corresponding author. Email: amccrani@indiana.edu
{"title":"Introduction to the Special Issue on Ego Networks","authors":"B. Perry, B. Pescosolido, M. Small, Ann McCranie","doi":"10.1017/nws.2020.18","DOIUrl":"https://doi.org/10.1017/nws.2020.18","url":null,"abstract":"1Department of Sociology and Indiana University Network Science Institute, Indiana University Bloomington, Bloomington IN, 47408, USA (e-mail: blperry@indiana.edu), 2Department of Sociology, Indiana Consortium for Mental Health Services, Indiana University Network Science Institute, Indiana University Bloomington, Bloomington IN, 47408, USA (e-mail: pescosol@indiana.edu), 3Department of Sociology, Harvard University, Cambridge, MA 02138, USA (e-mail: mariosmall@fas.harvard.edu), 4Indiana University Network Science Institute, Indiana University Bloomington, Bloomington IN, 47408, USA ∗Corresponding author. Email: amccrani@indiana.edu","PeriodicalId":51827,"journal":{"name":"Network Science","volume":"8 1","pages":"137 - 141"},"PeriodicalIF":1.7,"publicationDate":"2020-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.18","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43630288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sean M. Fitzhugh, Arwen H. DeCostanza, Norbou Buchler, Diane Ungvarsky
Abstract Individuals filling specialized, interdependent organizational roles achieve coordinated task execution through effective communication channels. Such channels enable regular access to information, opportunities, and assistance that may enhance one’s understanding of the task environment. However, the time and effort devoted to maintaining those channels may detract from one’s duties by turning attention away from the task environment. Disrupted task environments increase information requirements, thus creating a dilemma in which individuals must sustain benefits offered by important communication channels and relieve burdens imposed by ineffective channels. Using separable temporal exponential random graph models (STERGMs), this paper examines the relationship between situational awareness (SA) and the propensity to sustain or dissolve preexisting communication channels during 10 disruptive events experienced sequentially by a large, multifaceted military organization during a 2-week training exercise. Results provide limited evidence that increased SA detracts from tie preservation; instead SA begins to predict tie preservation during the second week of the exercise. Patterns of organizational adaptation reveal that, over time, improvised coordinative roles increasingly fall upon those with elevated SA. These results suggest that over successive disruptions, the benefits of information provided by communication channels within interdependent, role-specialized organizations begin to outweigh the costs of sustaining those channels.
{"title":"Cognition and communication: situational awareness and tie preservation in disrupted task environments","authors":"Sean M. Fitzhugh, Arwen H. DeCostanza, Norbou Buchler, Diane Ungvarsky","doi":"10.1017/nws.2020.15","DOIUrl":"https://doi.org/10.1017/nws.2020.15","url":null,"abstract":"Abstract Individuals filling specialized, interdependent organizational roles achieve coordinated task execution through effective communication channels. Such channels enable regular access to information, opportunities, and assistance that may enhance one’s understanding of the task environment. However, the time and effort devoted to maintaining those channels may detract from one’s duties by turning attention away from the task environment. Disrupted task environments increase information requirements, thus creating a dilemma in which individuals must sustain benefits offered by important communication channels and relieve burdens imposed by ineffective channels. Using separable temporal exponential random graph models (STERGMs), this paper examines the relationship between situational awareness (SA) and the propensity to sustain or dissolve preexisting communication channels during 10 disruptive events experienced sequentially by a large, multifaceted military organization during a 2-week training exercise. Results provide limited evidence that increased SA detracts from tie preservation; instead SA begins to predict tie preservation during the second week of the exercise. Patterns of organizational adaptation reveal that, over time, improvised coordinative roles increasingly fall upon those with elevated SA. These results suggest that over successive disruptions, the benefits of information provided by communication channels within interdependent, role-specialized organizations begin to outweigh the costs of sustaining those channels.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":"8 1","pages":"508 - 542"},"PeriodicalIF":1.7,"publicationDate":"2020-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.15","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43752832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract We discuss a two-week summer course on “Network Science” and “Complex Systems” that we taught for 15 German high-school pupils of ages 16–18. In this course, we covered topics in graph theory, applied network science, programming, and dynamic systems alike. We find that “Network Science” is a well-suited course for introducing students to university-level mathematics. We reflect on difficulties regarding programming exercises and the discussion of more advanced topics in dynamic systems. We make the course material available and encourage fellow network scientists to organize similar outreach events.
{"title":"Commentary: A network science summer course for high-school students","authors":"F. Klimm, B. Maier","doi":"10.1017/nws.2020.12","DOIUrl":"https://doi.org/10.1017/nws.2020.12","url":null,"abstract":"Abstract We discuss a two-week summer course on “Network Science” and “Complex Systems” that we taught for 15 German high-school pupils of ages 16–18. In this course, we covered topics in graph theory, applied network science, programming, and dynamic systems alike. We find that “Network Science” is a well-suited course for introducing students to university-level mathematics. We reflect on difficulties regarding programming exercises and the discussion of more advanced topics in dynamic systems. We make the course material available and encourage fellow network scientists to organize similar outreach events.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":"8 1","pages":"596 - 608"},"PeriodicalIF":1.7,"publicationDate":"2020-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.12","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47443909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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