The emergence of structure in interacting entities is a rich field of study and long been recognized as a defining characteristic of complex social networks. Scientific communities are not an exception to this observation. This short paper reports on the development of Python scripts for the Automatic Extraction of Academic Collaborations in Neuroimaging. This work leverages publicly available information on Google Scholar (GS) towards the automatic extraction of coauthorship networks.
Many have postulated that the success or failure of societies and organizations often depends on the patterns governing their internal structure. From this perspective, a network approach to the study of scientific communities benefits from the following properties: (1) it is guided by formal theory organized in mathematical terms, (2) grounded in the systematic analysis of empirical data, and (3) emerges from objective datasets [4]. At its core, the method attempts to capture patterns of human interactions by leveraging its network structure. One approach to quantifying such patterns is by extracting sub-graphs.
The tool can be accessed through a public website at Clubs of Science.
The site is constructed using a set of openly accessible libraries offering coauthorship networks in the form of interactive graphs. Visitors can peruse a set of pre-computed networks extracted using a set of custom Python scripts crawling Google Scholar.
Notes on how to compile this paper can be found at Brainhack-AMX