Network SciencePub Date : 2020-08-06DOI: 10.1017/nws.2020.26
Beate Volker
{"title":"Social capital across the life course: Accumulation, diminution, or segregation?","authors":"Beate Volker","doi":"10.1017/nws.2020.26","DOIUrl":"https://doi.org/10.1017/nws.2020.26","url":null,"abstract":"Abstract This study examines changes in individual social capital during adult life within a 19-year period. Social capital theory and life course theory are combined, and it is argued that changes in social networks do not necessarily go together with changes in social capital: while personal networks are known to decline in size with age, social capital can be expected to accumulate, in particular for those who had a better starting position and therefore more resources to share. Panel data from the survey of the social networks of the Dutch (SSND) (1999–2018) at four points of measurement are employed to inquire into this argument. Social capital is measured by the position generator instrument, and three indicators, that is, resource extensity, mean prestige access, and resource range are analyzed. Results of fixed effect models show that, on average, people maintain access to social capital, and that men and higher educated gain social capital through their life as opposed to women and lower educated. Implications for the understanding of the reproduction of social inequality are discussed. The paper concludes with a reflection upon the value of ego-centered network analysis in the era of big data and data science.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":"8 1","pages":"313 - 332"},"PeriodicalIF":1.7,"publicationDate":"2020-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.26","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49310361","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}
Network SciencePub Date : 2020-07-03DOI: 10.1017/nws.2020.23
Manuel Muñoz-Herrera, J. Dijkstra, A. Flache, R. Wittek
{"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}
Network SciencePub Date : 2020-07-01DOI: 10.1017/nws.2020.22
H. Cherifi, Luis Mateus Rocha, S. Wasserman
{"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}
{"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}
Network SciencePub Date : 2020-06-01Epub Date: 2019-11-04DOI: 10.1017/nws.2019.27
Bernie Hogan, Patrick Janulis, Gregory Lee Phillips, Joshua Melville, Brian Mustanski, Noshir Contractor, Michelle Birkett
{"title":"Assessing the stability of egocentric networks over time using the digital participant-aided sociogram tool Network Canvas.","authors":"Bernie Hogan, Patrick Janulis, Gregory Lee Phillips, Joshua Melville, Brian Mustanski, Noshir Contractor, Michelle Birkett","doi":"10.1017/nws.2019.27","DOIUrl":"https://doi.org/10.1017/nws.2019.27","url":null,"abstract":"<p><p>This paper examines the stability of egocentric networks as reported over time using a novel touchscreen-based participant-aided sociogram. Past work has noted the instability of nominated network alters, with a large proportion leaving and reappearing between interview observations. To explain this instability of networks over time, researchers often look to structural embeddedness, namely the notion that alters are connected to other alters within egocentric networks. Recent research has also asked whether the interview situation itself may play a role in conditioning respondents to what might be the appropriate size and shape of a social network, and thereby which alters ought to be nominated or not. We report on change in these networks across three waves and assess whether this change appears to be the result of natural churn in the network or whether changes might be the result of factors in the interview itself, particularly anchoring and motivated underreporting. Our results indicate little change in average network size across waves, particularly for indirect tie nominations. Slight, significant changes were noted between waves one and two particularly among those with the largest networks. Almost no significant differences were observed between waves two and three, either in terms of network size, composition, or density. Data come from three waves of a Chicago-based panel study of young men who have sex with men.</p>","PeriodicalId":51827,"journal":{"name":"Network Science","volume":"8 2","pages":"204-222"},"PeriodicalIF":1.7,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2019.27","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25408447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Network SciencePub Date : 2020-06-01Epub Date: 2019-11-04DOI: 10.1017/nws.2019.29
Raffaele Vacca
{"title":"Structure in personal networks: Constructing and comparing typologies.","authors":"Raffaele Vacca","doi":"10.1017/nws.2019.29","DOIUrl":"https://doi.org/10.1017/nws.2019.29","url":null,"abstract":"<p><p>A recurrent finding in personal network research is that individual and social outcomes are influenced not just by the kind of people one knows, but also by how those people are connected to each other: that is, by the structure of one's personal network. The different ways in which a person's social contacts know and interact with each other reflect broader variations in personal communities and social structures, and shape patterns and processes of social capital, support, and isolation. This article proposes a method to identify typologies of network structure in large collections of personal networks. The method is illustrated with an application to six datasets collected in widely different circumstances and using various survey instruments. Results are compared with those from another recently introduced method to extract structural typologies of egocentric networks. Findings show that personal network structure can be effectively summarized using just three measures describing results of the Girvan-Newman algorithm for cohesive subgroup detection. Structural typologies can then be extracted through cluster analysis on the three variables, using well-known clustering quality statistics to select the optimal typology. Both typology detection methods considered in the article capture significant variation in personal network structures, but substantial levels of disagreement and cross-classification emerge between them. I discuss differences and similarities between the methods, and potential applications of the proposed typologies to substantive research on a variety of topics, including structures and transformations of personal communities, social support, and social capital.</p>","PeriodicalId":51827,"journal":{"name":"Network Science","volume":"8 2","pages":"142-167"},"PeriodicalIF":1.7,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2019.29","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38630371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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