The relationship between acquaintanceship and coauthorship in scientific collaboration networks

This article examines the relationship between acquaintanceship and coauthorship patterns in a multi-disciplinary, multi-institutional, geographically distributed research center. Two social networks are constructed and compared: a network of coauthorship, representing how researchers write articles with one another, and a network of acquaintanceship, representing how those researchers know each other on a personal level, based on their responses to an online survey. Statistical analyses of the topology and community structure of these networks point to the importance of small-scale, local, personal networks predicated upon acquaintanceship for accomplishing collaborative work in scientific communities

Exploring cooperative game mechanisms of scientific coauthorship networks

Scientific coauthorship, generated by collaborations and competitions among researchers, reflects effective organizations of human resources. Researchers, their expected benefits through collaborations, and their cooperative costs constitute the elements of a game. Hence we propose a cooperative game model to explore the evolution mechanisms of scientific coauthorship networks. The model generates geometric hypergraphs, where the costs are modelled by space distances, and the benefits are expressed by node reputations, i. e. geometric zones that depend on node position in space and time. Modelled cooperative strategies conditioned on positive benefit-minus-cost reflect the spatial reciprocity principle in collaborations, and generate high clustering and degree assortativity, two typical features of coauthorship networks. Modelled reputations generate the generalized Poisson parts and fat tails appeared in specific distributions of empirical data, e. g. paper team size distribution. The combined effect of modelled costs and reputations reproduces the transitions emerged in degree distribution, in the correlation between degree and local clustering coefficient, etc. The model provides an example of how individual strategies induce network complexity, as well as an application of game theory to social affiliation networks

COLLABORATING TO RUIN? US NATIONAL LABORATORIES AND THE IMPACT OF INTERNATIONAL RESEARCH PARTNERSHIPS

Following the Cold War, Russian and US research institutions forged new collaborative ties to take advantage of perceived complementarities in conducting scientific research as part of US nonproliferation initiatives. These ties appear to have been successful in the broader nonproliferation context as relatively few Russian nuclear scientists emigrated to perceived rogue states like Iran and North Korea in the years that immediately followed the dissolution of the Soviet Union. Early on, the research benefits of these ties appeared to be significant. Today, as the Russian science and technology cadre is going through a demographic transition and the Russian state is following a corporatist policy in rebuilding its scientific research and development base, the appropriable benefits associated with continuing these policies for US research partners are less obvious. This assessment is an attempt to gain an empirical understanding of the appropriable benefits from US-Russian research engagement apart from the nonproliferation context. As such, this study examines these collaborations using an alternative network analysis methodology with reference to a knowledge-based model of research and development generation. To assure tractability, the analysis focuses its attention on a subset of institutions that have been broadly ignored in studies of research collaboration — US national laboratories and their Russian counterparts. The resulting analysis challenges the conventional wisdom of the appropriable virtues of scientific collaboration. For the limited set of relationships examined in this study, this analysis suggests participation in international collaborations between the largest US national laboratories and their Russian counterparts can actually reduce individual researchers basic research productivity — clearly not a policy goal for a major national research and development establishment. To achieve better appropriability, this finding and its contextual factors are used to demarcate areas of inquiry where Russian-US engagement has an empirical track record of utility and should continue from areas where collaboration has had little success

Co-authorship, Homophily, and Scholarly Influence in Information Systems Research

Information Systems (IS) researchers have increasingly focused attention on understanding the identity of our field (Hirschheim & Klein 2003; Lyytinen & King 2004). One facet of any discipline’s identity is the social aspect of how its scholars actually conduct their work (DeSanctis 2003), which is formally labeled as the study of sociology of science. Contributing to this tradition of work, we empirically examine scholarly influence (Acedo et al., 2006); scientific collaboration, including metrics that capture the prevalence of c-oauthored work; antecedents to co-authorship; and the effect of co-authorship on subsequent citations. Based on analyzing five leading IS journals for a period of seven years, we found that co-authored papers have become increasingly common in leading IS journals and that co-authoring continues to be more prevalent in journals published in North America compared to European journals. Moreover, we found significant effects of homophily related to gender, homophily/proximity, and geography. IS scholars worldwide exhibit a stronger preference for collaborating with co-authors of the same sex and those who attended the same PhD program than one would expect by chance. We also examined differences among journals and found some intriguing results for the effect of co-authorship on citations. Overall, we found evidence that the number of co-authors was positively related to citations although there was some variance across journals. These findings point to a need for more research to better understand both the processes of collaboration and the drivers and downstream benefits associated with it

SOCIAL NETWORK ANALYSIS FOR DIGITAL HUMANITIES

Current trends in academia show that a key factor for tackling complex problems and doing successful research is interdisciplinarity. With the increasing availability of digital tools and online databases, many disciplines in the humanities and social sciences are seeking to incorporate computational techniques in their research workflow. Digital humanities (DH) is a collaborative and interdisciplinary area of research that bridges computing and the humanities disciplines, bringing digital tools to humanities scholars to use, together with a critical understanding of such tools. Social network analysis is one of such tools. Social network analysis focuses on relationships among social actors and it is an important addition to standard social and behavioral research, which is primarily concerned with attributes of the social units. In this work we present the field of digital humanities and its current challenges, as well as an overview of the most recent trends in historical network research, emphasizing the advantages of using social network analysis in history and the missed opportunities. We then present the field of network analysis, providing a formalization of the concept of social network, models that explain the mechanism governing complex networks and tools such as network metrics, orbit analysis and Exponential Random Graph Model. We tackle the problem of community detection. We propose MemLPA, a new version of the label propagation algorithm, by incorporating a memory element, in order for nodes to consider past states of the network in their decision rule. We present a use case, drawn from the collaboration with a historian colleague, showing how social network analysis can be used to answer research questions in history. In particular, we addressed the gender and ethnic bias problem in computer science research by looking at different collaboration patterns in the temporal co-authorship network. Finally, we present another use case, based on collaboration data collected at the National Electronics and Computer Technology Center (NECTEC) in Thailand. We build a temporal collaboration network where researchers are connected if they worked together on one or more artifacts, focusing on measuring productivity and quality of research and development, while linking these metrics to the structure of the collaboration network

Assessing the Effect of Social Networks on Employee Creativity in a Fast-Food Restaurant Environment

Creativity has been widely recognized as critical to the economic success of organizations for over 60 years. Today, it is considered to be the most highly prized commodity of businesses. As such, there have been numerous efforts to better understand creativity with the goal of increasing individual creativity and therefore improving the economic success of organizations. An emerging area of research on creativity recognizes creativity as a complex, social process that is dependent upon many factors, including those of an environmental nature. In support of this perspective, a growing amount of research has investigated the effect of social networks on individual creativity. This relationship is based on the premise that an individual\u27s social network affects access to diverse information, which in turn, is critical for creativity. The previous studies on this relationship, however, have been conducted in a limited number of environments, most of which have been knowledge-intensive in nature. As such, this study was conducted in a fast-food restaurant environment to determine whether the relationship between social networks and creativity is the same as in other, previously studied environments. Data was collected for a sample of 247 employees of an organization consisting of seven fast-food franchise restaurants of a popular fast-food restaurant chain in the northeast region of the United States. An ordinary least squares regression model was developed to investigate the relationship between creativity and the commonly studied social network variables: number of weak ties, number of strong ties, clustering, and centrality. The social network variables accounted for 17.3% of the overall variance in creativity, establishing that a relationship does exist between social networks and creativity in the fast-food restaurant environment. This relationship, however, was not as expected. In contrast to expectations, weak ties were not found to be a significant, positive predictor of creativity. Also, strong ties were found to be a significant, positive predictor of creativity, where it was expected that this relationship would be in the negative direction. Centrality, however, was found to be a significant, positive predictor of creativity, as expected, while the results for clustering were inconclusive due to its high correlation with the other social network variables in the study. As such, it appears that the relationship between social networks and creativity may be different in the fast-food restaurant environment when compared to environments previously studied. It is possible that this difference is a result of the differences between high and low knowledge-intensive working environments. The lack of support for weak ties as a significant positive predictor of creativity in conjunction with limited opportunities for significant creative achievement suggests that access to diverse information may be less important for creativity in the fast-food restaurant environment than in other environments. The findings that strong ties and centrality are significant, positive predictors of creativity, however, appear to indicate that the ability to implement a creative idea, however minor it may be, is more important in the fast-food restaurant environment than the generation of that idea in the first place. Due to the limitations of this study, however, it is not possible to definitively conclude this notion without efforts to determine which factor afforded by positions rich in strong ties or high in centrality, the informational benefits or the organizational influence, is more important for creativity

The Geography of Scientific Collaboration

Science is increasingly defined by multidimensional collaborative networks. Despite the unprecedented growth of scientific collaboration around the globe – the collaborative turn – geography still matters for the cognitive enterprise. This book explores how geography conditions scientific collaboration and how collaboration affects the spatiality of science. This book offers a complex analysis of the spatial aspects of scientific collaboration, addressing the topic at a number of levels: individual, organizational, urban, regional, national, and international. Spatial patterns of scientific collaboration are analysed along with their determinants and consequences. By combining a vast array of approaches, concepts, and methodologies, the volume offers a comprehensive theoretical framework for the geography of scientific collaboration. The examples of scientific collaboration policy discussed in the book are taken from the European Union, the United States, and China. Through a number of case studies the authors analyse the background, development and evaluation of these policies. This book will be of interest to researchers in diverse disciplines such as regional studies, scientometrics, R&D policy, socio-economic geography and network analysis. It will also be of interest to policymakers, and to managers of research organisations

The Geography of Scientific Collaboration

Science is increasingly defined by multidimensional collaborative networks. Despite the unprecedented growth of scientific collaboration around the globe – the collaborative turn – geography still matters for the cognitive enterprise. This book explores how geography conditions scientific collaboration and how collaboration affects the spatiality of science. This book offers a complex analysis of the spatial aspects of scientific collaboration, addressing the topic at a number of levels: individual, organizational, urban, regional, national, and international. Spatial patterns of scientific collaboration are analysed along with their determinants and consequences. By combining a vast array of approaches, concepts, and methodologies, the volume offers a comprehensive theoretical framework for the geography of scientific collaboration. The examples of scientific collaboration policy discussed in the book are taken from the European Union, the United States, and China. Through a number of case studies the authors analyse the background, development and evaluation of these policies. This book will be of interest to researchers in diverse disciplines such as regional studies, scientometrics, R&D policy, socio-economic geography and network analysis. It will also be of interest to policymakers, and to managers of research organisations