Studying the emerging global brain: Analyzing and visualizing the impact of co-authorship teams

This paper introduces a suite of approaches and measures to study the impact of co-authorship teams based on the number of publications and their citations on a local and global scale. In particular, we present a novel weighted graph representation that encodes coupled author-paper networks as a weighted co-authorship graph. This weighted graph representation is applied to a dataset that captures the emergence of a new field of science and comprises 614 papers published by 1,036 unique authors between 1974 and 2004. In order to characterize the properties and evolution of this field we first use four different measures of centrality to identify the impact of authors. A global statistical analysis is performed to characterize the distribution of paper production and paper citations and its correlation with the co-authorship team size. The size of co-authorship clusters over time is examined. Finally, a novel local, author-centered measure based on entropy is applied to determine the global evolution of the field and the identification of the contribution of a single author’s impact across all of its co-authorship relations. A visualization of the growth of the weighted co-author network and the results obtained from the statistical analysis indicate a drift towards a more cooperative, global collaboration process as the main drive in the production of scientific knowledge. Number of text pages: 12 Number of figures: 9 Number of tables: 2 Summary of results for the nonspecialist: The increasing specialization of researchers and practitioners, the accelerated speed of scientific progress and the need to collaborate across institutional and disciplinary boundaries is fostering the formation of high-impact coauthorship teams. This paper presents novel methods to analyze and visualize the size, interconnections and impact of co-author teams over time. The methods were applied to examine the evolution of a 31-year publication dataset that captures the emergence of a new scientific field. The results indicate the development of a more interdisciplinary, globally connected science as opposed to science driven by single experts.