Abstract
The availability of co-authorship data from large-scale electronic databases is paving the way for new analyses on human collaboration networks. The complex network of co-authorships can identify specific features that characterise the behaviour of researchers, and impact on their production and performance. In this paper, we analyse a large sample of data regarding scientific publications from Google Scholar. The aim of our analysis is to study a fundamental aspect of co-authorship networks, i.e. the structure of authors’ ego networks. Specifically, we highlight the existence of a hierarchical organisation of these networks in a series of concentric circles, quite similar to that found in general human social networks. In addition, we highlight some properties of the correlation between the ego network structure and the authors scientific productivity, measured in terms of h-index.
This work was partially funded by the EC under the H2020-INFRAIA SoBigData (654024) project.
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Arnaboldi, V., Dunbar, R.I.M., Passarella, A., Conti, M. (2016). Analysis of Co-authorship Ego Networks. In: Wierzbicki, A., Brandes, U., Schweitzer, F., Pedreschi, D. (eds) Advances in Network Science. NetSci-X 2016. Lecture Notes in Computer Science(), vol 9564. Springer, Cham. https://doi.org/10.1007/978-3-319-28361-6_7
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