Drawing the shape of scientific collaborations through topological data analysis (a scientific paper)

The structure of scientific collaborations has been the object of intense study both for its importance for innovation and scientific advancement, and as a model system for social group coordination and formation. Over the last years, the predominant complex networks approach has yielded important insights and shaped the understanding of scientific communities. In a new article in EPJ Data Science, former ISI researcher Alice Patania (IU Network Institute, Indiana University), ISI Principal Researcher Giovanni Petri and ISI Senior Researcher Francesco Vaccarino propose to complement the picture provided by network tools with that coming from topological data analysis, giving a fresh look into the dynamics of scientific collaborations.

The topological approach allows scientists to go beyond the “k-clique descriptions”, as it can easily distinguish between sums of pairwise interactions, and genuine higher-order ones. Without relying on local properties or global distributions, it enables them to uncover the mesoscopic properties of the data set (an arXiv collection of articles, with timespan from 2007 to 2016) through new tools like homology, which encodes a notion of multi-dimensional shape. The paper also shows that it is natural to extend the concept of triadic closure to simplicial complexes, highlighting the presence of strong simplicial closure.

arXiv is structured in 18 different scientific fields. Results show that, while major categories are characterized by different collaboration size distributions, the distributions of the number of collaborations an author participates to appears much more similar. Moreover, while categories are characterized by organizational and cultural differences, the individual capacity to participate in collaborations is similar across categories. Authors in experimental categories tend to collaborate in larger, not fully overlapping groups. In contrast, in the most theoretical communities of the same disciplines, the collaboration groups tend to have slower turnover of members over time and smaller, repeated collaborations within larger groups.

“The shape of collaborations”, Alice Petri, Giovanni Patania and Francesco Vaccarino, EPJ Data Science, 24th August 2017, full paper available at https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-017-0114-8.