Pub Date : 2021-01-01DOI: 10.21307/CONNECTIONS-2021.022
Andrew Pilny, Lin Xiang, C. Huber, Will R Silberman, Sean Goatley-Soan
Abstract At its core, contact tracing is a form of egocentric network analysis (ENA). One of the biggest obstacles for ENA is informant accuracy (i.e., amount of true contacts identified), which is even more prominent for interaction-based network ties because they often represent episodic relational events, rather than enduring relational states. This research examines the effect of informant accuracy on the spread of COVID-19 through an egocentric, agent-based model. Overall when the average person transmits COVID-19 to 1.62 other people (i.e., the R0), they must be, on average, 75% accurate with naming their contacts. In higher transmission contexts (i.e., transmitting to at least two other people), the results show that multi-level tracing (i.e., contact tracing the contacts) is the only viable strategy. Finally, sensitivity analysis shows that the effectiveness of contact tracing is negatively impacted by the timing and overall percent of asymptomatic cases. Overall, the results suggest that if contact tracing is to be effective, it must be fast, accurate, and accompanied by other interventions like mask-wearing to drive down the average R0.
{"title":"The impact of contact tracing on the spread of COVID-19: an egocentric agent-based model","authors":"Andrew Pilny, Lin Xiang, C. Huber, Will R Silberman, Sean Goatley-Soan","doi":"10.21307/CONNECTIONS-2021.022","DOIUrl":"https://doi.org/10.21307/CONNECTIONS-2021.022","url":null,"abstract":"Abstract At its core, contact tracing is a form of egocentric network analysis (ENA). One of the biggest obstacles for ENA is informant accuracy (i.e., amount of true contacts identified), which is even more prominent for interaction-based network ties because they often represent episodic relational events, rather than enduring relational states. This research examines the effect of informant accuracy on the spread of COVID-19 through an egocentric, agent-based model. Overall when the average person transmits COVID-19 to 1.62 other people (i.e., the R0), they must be, on average, 75% accurate with naming their contacts. In higher transmission contexts (i.e., transmitting to at least two other people), the results show that multi-level tracing (i.e., contact tracing the contacts) is the only viable strategy. Finally, sensitivity analysis shows that the effectiveness of contact tracing is negatively impacted by the timing and overall percent of asymptomatic cases. Overall, the results suggest that if contact tracing is to be effective, it must be fast, accurate, and accompanied by other interventions like mask-wearing to drive down the average R0.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"41 1","pages":"25 - 46"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67974625","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}
Pub Date : 2021-01-01DOI: 10.21307/connections-2021.023
Darren Colby
Abstract The effect of leadership decapitation—the capture or killing of the leader of an armed group—on future violence has been studied with competing conclusions. In Mexico, leadership decapitation has been found to increase violence and in-fighting among drug cartels. However, the causal pathways between leadership decapitation and in-fighting are unclear. In this article, it is hypothesized that leadership decapitation will weaken alliances between armed actors, lead to greater preferential attachment in networks of cartels and militias, and result in greater transitive closure as cartels seek to expand their power. These hypotheses are tested with a stochastic actor oriented model on a network dataset of episodes of infighting among cartels and the militias formed to opposed them between the five years before and after Joaquín, “El Chapo” Guzmán Loera, the former leader of the Sinaloa Cartel, was arrested in 2016. The results show that alliances have virtually no effect on the decision of cartels and militias to fight each other; weaker organizations faced a higher reputational cost after El Chapo’s detention; and post-arrest cartel in-fighting did not increase as a result of uncertainty about the relative balance of power among cartels.
{"title":"Chaos from order: a network analysis of in-fighting before and after El Chapo’s arrest","authors":"Darren Colby","doi":"10.21307/connections-2021.023","DOIUrl":"https://doi.org/10.21307/connections-2021.023","url":null,"abstract":"Abstract The effect of leadership decapitation—the capture or killing of the leader of an armed group—on future violence has been studied with competing conclusions. In Mexico, leadership decapitation has been found to increase violence and in-fighting among drug cartels. However, the causal pathways between leadership decapitation and in-fighting are unclear. In this article, it is hypothesized that leadership decapitation will weaken alliances between armed actors, lead to greater preferential attachment in networks of cartels and militias, and result in greater transitive closure as cartels seek to expand their power. These hypotheses are tested with a stochastic actor oriented model on a network dataset of episodes of infighting among cartels and the militias formed to opposed them between the five years before and after Joaquín, “El Chapo” Guzmán Loera, the former leader of the Sinaloa Cartel, was arrested in 2016. The results show that alliances have virtually no effect on the decision of cartels and militias to fight each other; weaker organizations faced a higher reputational cost after El Chapo’s detention; and post-arrest cartel in-fighting did not increase as a result of uncertainty about the relative balance of power among cartels.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"41 1","pages":"1 - 11"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43703985","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}
Pub Date : 2021-01-01DOI: 10.21307/CONNECTIONS-2019.020
Jeroen Bruggeman, R. Corten
Abstract A cohesive network keeps groups together and enables members to communicate about and cooperate for public goods. For ongoing cooperation, group members have to know if their group members cooperate or defect, but this information—mostly through gossip—is threatened by noise and biases. If there are redundant information channels, however, errors in monitoring and transmission in one imperfect channel can, to some degree, be corrected by information through another imperfect channel, and may lead to higher levels of cooperation. An influential conceptualization of social cohesion based on redundancy is K-connectivity: the minimum number (K) of node-independent paths connecting pairs of nodes in a group’s network. In a lab experiment, we tested if higher K-connectivity yields higher levels of cooperation for public goods, controlling for a number of other network effects such as density, size, and average distance. We do not find the hypothesized effect, which might be due to a not-earlier-found shortcoming of the concept, and we propose a solution.
{"title":"Social Cohesion and Cooperation for Public Goods","authors":"Jeroen Bruggeman, R. Corten","doi":"10.21307/CONNECTIONS-2019.020","DOIUrl":"https://doi.org/10.21307/CONNECTIONS-2019.020","url":null,"abstract":"Abstract A cohesive network keeps groups together and enables members to communicate about and cooperate for public goods. For ongoing cooperation, group members have to know if their group members cooperate or defect, but this information—mostly through gossip—is threatened by noise and biases. If there are redundant information channels, however, errors in monitoring and transmission in one imperfect channel can, to some degree, be corrected by information through another imperfect channel, and may lead to higher levels of cooperation. An influential conceptualization of social cohesion based on redundancy is K-connectivity: the minimum number (K) of node-independent paths connecting pairs of nodes in a group’s network. In a lab experiment, we tested if higher K-connectivity yields higher levels of cooperation for public goods, controlling for a number of other network effects such as density, size, and average distance. We do not find the hypothesized effect, which might be due to a not-earlier-found shortcoming of the concept, and we propose a solution.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"41 1","pages":"1 - 6"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46653412","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}
Pub Date : 2020-01-01DOI: 10.21307/connections-2019.015
I. Maya-Jariego
Abstract The book “Conducting Personal Network Research” is a conceptual and methodological introduction to the structural study of personal networks. It is part of a series of recent monographs that have begun to systematize the knowledge generated in this area in recent decades (Crossley et al., 2015; McCarty et al., 2019; Perry et al., 2018). In this case, the authors have dedicated a large part of their career to the empirical investigation of the interpersonal relationships, interaction contexts, and social integration processes of immigrants, along with other groups in vulnerable situations. With this publication, all this experience is now reflected in a clear and comprehensive introductory text. This book explains how to integrate relational data collection and analysis with survey research. It systematically presents the strategies to estimate the size of personal networks. Finally, it describes how to fit statistical analysis to relational data, including regression models, multi-level models, and longitudinal models.
{"title":"Commentary: How to do personal network surveys: from name generators to statistical modeling","authors":"I. Maya-Jariego","doi":"10.21307/connections-2019.015","DOIUrl":"https://doi.org/10.21307/connections-2019.015","url":null,"abstract":"Abstract The book “Conducting Personal Network Research” is a conceptual and methodological introduction to the structural study of personal networks. It is part of a series of recent monographs that have begun to systematize the knowledge generated in this area in recent decades (Crossley et al., 2015; McCarty et al., 2019; Perry et al., 2018). In this case, the authors have dedicated a large part of their career to the empirical investigation of the interpersonal relationships, interaction contexts, and social integration processes of immigrants, along with other groups in vulnerable situations. With this publication, all this experience is now reflected in a clear and comprehensive introductory text. This book explains how to integrate relational data collection and analysis with survey research. It systematically presents the strategies to estimate the size of personal networks. Finally, it describes how to fit statistical analysis to relational data, including regression models, multi-level models, and longitudinal models.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"40 1","pages":"98 - 102"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43887487","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}
Pub Date : 2020-01-01DOI: 10.21307/connections-2019.013
E. Lazega
Abstract This paper is the written text underlying the keynote presentation at the Sunbelt XXXVIII in Utrecht, 2018. It presents a neo-structural approach to social processes in the organizational society and the usefulness of the analyses of multilevel networks to understand how we navigate these processes and are made aware of them when we face cooperation dilemmas. Empirical illustrations look at how multilevel networks and relational infrastructures are useful to research a process such as coopetitive learning in science, business and government. A conclusion focuses on the role of multilevel relational infrastructures in institutional entrepreneurship, social change and politics, as well as on our responsibility to develop our knowledge of these social processes and multilevel relational infrastructures as open science.
{"title":"Embarked on social processes (the rivers) in dynamic and multilevel networks (the boats)","authors":"E. Lazega","doi":"10.21307/connections-2019.013","DOIUrl":"https://doi.org/10.21307/connections-2019.013","url":null,"abstract":"Abstract This paper is the written text underlying the keynote presentation at the Sunbelt XXXVIII in Utrecht, 2018. It presents a neo-structural approach to social processes in the organizational society and the usefulness of the analyses of multilevel networks to understand how we navigate these processes and are made aware of them when we face cooperation dilemmas. Empirical illustrations look at how multilevel networks and relational infrastructures are useful to research a process such as coopetitive learning in science, business and government. A conclusion focuses on the role of multilevel relational infrastructures in institutional entrepreneurship, social change and politics, as well as on our responsibility to develop our knowledge of these social processes and multilevel relational infrastructures as open science.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"40 1","pages":"60 - 76"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46687811","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}
Pub Date : 2020-01-01DOI: 10.21307/connections-2019.011
Denis Trapido, F. Pallotti, A. Lomi
Abstract Organizations have leeway in how much they employ their network relations to the benefit of their clients. When do they do so more rather than less? Relying on research on trust and knowledge absorption, the authors suggest that providers’ network relations generate better outcomes for their clients when these relations are concentrated in a limited, exclusive set of partners. The authors argue that providers’ relational exclusivity benefits clients because it facilitates the awareness and use of partners’ complementary client service capabilities. An analysis of a regional network of patient referrals among 110 hospitals supported this argument. The study highlights the role of interorganizational partnership networks in activating client service capabilities and stimulates further inquiry into providers’ network features that benefit the users of their services.
{"title":"Clients’ outcomes from providers’ networks: the role of relational exclusivity and complementary capabilities","authors":"Denis Trapido, F. Pallotti, A. Lomi","doi":"10.21307/connections-2019.011","DOIUrl":"https://doi.org/10.21307/connections-2019.011","url":null,"abstract":"Abstract Organizations have leeway in how much they employ their network relations to the benefit of their clients. When do they do so more rather than less? Relying on research on trust and knowledge absorption, the authors suggest that providers’ network relations generate better outcomes for their clients when these relations are concentrated in a limited, exclusive set of partners. The authors argue that providers’ relational exclusivity benefits clients because it facilitates the awareness and use of partners’ complementary client service capabilities. An analysis of a regional network of patient referrals among 110 hospitals supported this argument. The study highlights the role of interorganizational partnership networks in activating client service capabilities and stimulates further inquiry into providers’ network features that benefit the users of their services.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"40 1","pages":"31 - 46"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41779403","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}
Pub Date : 2020-01-01DOI: 10.21307/connections-2019.018
Ian Kim, T. Valente
Abstract Coronavirus disease of 2019 (COVID-19)’s devastating effects on the physical and mental health of the public are unlike previous medical crises, in part because of people’s collective access to communication technologies. Unfortunately, a clear understanding of the diffusion of health information on social media is lacking, which has a potentially negative impact on the effectiveness of emergency communication. This study applied social network analysis approaches to examine patterns of #COVID19 information flow on Twitter. A total of 1,404,496 publicly available tweets from 946,940 U.S. users were retrieved and analyzed. Particular attention was paid to the structures of retweet and mention networks and identification of influential users: information sources, disseminators, and brokers. Overall, COVID-19 information was not transmitted efficiently. Findings pointed to the importance of fostering connections between clusters to promote the diffusion in both networks. Lots of localized clusters limited the spread of timely information, causing difficulty in establishing any momentum in shaping urgent public actions. Rather than health and communication professionals, there was dominant involvement of non-professional users responsible for major COVID-19 information generation and dissemination, suggesting a lack of credibility and accuracy in the information. Inadequate influence of health officials and government agencies in brokering information contributed to concerns about the spread of dis/misinformation to the public. Significant differences in the type of influential users existed across roles and across networks. Conceptual and practical implications for emergency communication strategies are discussed.
{"title":"COVID-19 Health Communication Networks on Twitter: Identifying Sources, Disseminators, and Brokers","authors":"Ian Kim, T. Valente","doi":"10.21307/connections-2019.018","DOIUrl":"https://doi.org/10.21307/connections-2019.018","url":null,"abstract":"Abstract Coronavirus disease of 2019 (COVID-19)’s devastating effects on the physical and mental health of the public are unlike previous medical crises, in part because of people’s collective access to communication technologies. Unfortunately, a clear understanding of the diffusion of health information on social media is lacking, which has a potentially negative impact on the effectiveness of emergency communication. This study applied social network analysis approaches to examine patterns of #COVID19 information flow on Twitter. A total of 1,404,496 publicly available tweets from 946,940 U.S. users were retrieved and analyzed. Particular attention was paid to the structures of retweet and mention networks and identification of influential users: information sources, disseminators, and brokers. Overall, COVID-19 information was not transmitted efficiently. Findings pointed to the importance of fostering connections between clusters to promote the diffusion in both networks. Lots of localized clusters limited the spread of timely information, causing difficulty in establishing any momentum in shaping urgent public actions. Rather than health and communication professionals, there was dominant involvement of non-professional users responsible for major COVID-19 information generation and dissemination, suggesting a lack of credibility and accuracy in the information. Inadequate influence of health officials and government agencies in brokering information contributed to concerns about the spread of dis/misinformation to the public. Significant differences in the type of influential users existed across roles and across networks. Conceptual and practical implications for emergency communication strategies are discussed.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"40 1","pages":"129 - 142"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41688213","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}
Pub Date : 2020-01-01DOI: 10.21307/CONNECTIONS-2019.009
Patrick D. Allen, Mark Alan Matties, Elisha Peterson
Abstract Hairball buster (HB) (also called node-neighbor centrality or NNC) is an approach to graph analytic triage that uses simple calculations and visualization to quickly understand and compare graphs. Rather than displaying highly interconnected graphs as ‘hairballs’ that are difficult to understand, HB provides a simple standard visual representation of a graph and its metrics, combining a monotonically decreasing curve of node metrics with indicators of each node’s neighbors’ metrics. The HB visual is canonical, in the sense that it provides a standard output for each node-link graph. It helps analysts quickly identify areas for further investigation, and also allows for easy comparison between graphs of different data sets. The calculations required for creating an HB display is order M plus N log N, where N is the number of nodes and M is the number of edges. This paper includes examples of the HB approach applied to four real-world data sets. It also compares HB to similar visual approaches such as degree histograms, adjacency matrices, blockmodeling, and force-based layout techniques. HB presents greater information density than other algorithms at lower or equal calculation cost, efficiently presenting information in a single display that is not available in any other single display.
Hairball buster (HB)(也称为节点邻居中心性或NNC)是一种图形分析分类方法，它使用简单的计算和可视化来快速理解和比较图形。HB没有将高度互连的图形显示为难以理解的“毛球”，而是提供了图形及其指标的简单标准可视化表示，将节点指标的单调递减曲线与每个节点邻居指标的指标相结合。HB可视化是规范的，因为它为每个节点链接图提供了标准输出。它可以帮助分析人员快速确定需要进一步调查的领域，还可以方便地比较不同数据集的图形。创建HB显示所需的计算是o (M + N log N)，其中N是节点的数量，M是边的数量。本文包括HB方法应用于四个真实世界数据集的例子。它还将HB与类似的视觉方法进行了比较，如度直方图、邻接矩阵、块建模和基于力的布局技术。与其他算法相比，HB以更低或相同的计算成本提供了更大的信息密度，有效地在单个显示器中显示任何其他单个显示器无法提供的信息。
{"title":"Hairball Buster: A Graph Triage Method for Viewing and Comparing Graphs","authors":"Patrick D. Allen, Mark Alan Matties, Elisha Peterson","doi":"10.21307/CONNECTIONS-2019.009","DOIUrl":"https://doi.org/10.21307/CONNECTIONS-2019.009","url":null,"abstract":"Abstract Hairball buster (HB) (also called node-neighbor centrality or NNC) is an approach to graph analytic triage that uses simple calculations and visualization to quickly understand and compare graphs. Rather than displaying highly interconnected graphs as ‘hairballs’ that are difficult to understand, HB provides a simple standard visual representation of a graph and its metrics, combining a monotonically decreasing curve of node metrics with indicators of each node’s neighbors’ metrics. The HB visual is canonical, in the sense that it provides a standard output for each node-link graph. It helps analysts quickly identify areas for further investigation, and also allows for easy comparison between graphs of different data sets. The calculations required for creating an HB display is order M plus N log N, where N is the number of nodes and M is the number of edges. This paper includes examples of the HB approach applied to four real-world data sets. It also compares HB to similar visual approaches such as degree histograms, adjacency matrices, blockmodeling, and force-based layout techniques. HB presents greater information density than other algorithms at lower or equal calculation cost, efficiently presenting information in a single display that is not available in any other single display.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"40 1","pages":"1 - 24"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45076101","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}
Pub Date : 2020-01-01DOI: 10.21307/CONNECTIONS-2019.019
E. Lazega
Abstract This picture, produced by Julien Brailly et al. (2016) and David Schoch (2020), visualizes multilevel networks of individuals and organizations.
{"title":"Visualizing Multilevel Networks for the Analysis of Superposed Levels of Collective Agency","authors":"E. Lazega","doi":"10.21307/CONNECTIONS-2019.019","DOIUrl":"https://doi.org/10.21307/CONNECTIONS-2019.019","url":null,"abstract":"Abstract This picture, produced by Julien Brailly et al. (2016) and David Schoch (2020), visualizes multilevel networks of individuals and organizations.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"40 1","pages":"143 - 145"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43133151","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}
Pub Date : 2020-01-01DOI: 10.21307/CONNECTIONS-2019.010
M. Bojanowski, Dominika Czerniawska, Wojciech Fenrich
Abstract In order to understand scientists’ incentives to form collaborative relations, we have conducted a study looking into academically relevant resources, which scientists contribute into collaborations with others. The data we describe in this paper are an egocentric dataset assembled by coding originally qualitative material. It is 40 multiplex ego networks containing data on individual attributes (such as gender, scientific degree), collaboration ties (including alter–alter ties), and resource flows. Resources are coded using a developed inventory of 25 types of academically relevant resources egos and alters contribute into their collaborations. We share the data with the research community with the hopes of enriching knowledge and tools for studying sociological and behavioral aspects of science as a social process.
{"title":"Academic Collaboration via Resource Contributions: An Egocentric Dataset","authors":"M. Bojanowski, Dominika Czerniawska, Wojciech Fenrich","doi":"10.21307/CONNECTIONS-2019.010","DOIUrl":"https://doi.org/10.21307/CONNECTIONS-2019.010","url":null,"abstract":"Abstract In order to understand scientists’ incentives to form collaborative relations, we have conducted a study looking into academically relevant resources, which scientists contribute into collaborations with others. The data we describe in this paper are an egocentric dataset assembled by coding originally qualitative material. It is 40 multiplex ego networks containing data on individual attributes (such as gender, scientific degree), collaboration ties (including alter–alter ties), and resource flows. Resources are coded using a developed inventory of 25 types of academically relevant resources egos and alters contribute into their collaborations. We share the data with the research community with the hopes of enriching knowledge and tools for studying sociological and behavioral aspects of science as a social process.","PeriodicalId":88856,"journal":{"name":"Connections (Toronto, Ont.)","volume":"40 1","pages":"25 - 30"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41981044","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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