Hyperlinks as inter-university collaboration indicators

Collaboration is essential for some types of research, and some agencies include collaboration among the requirements for funding research projects. This makes it important to analyse collaborative research ties. Traditional methods to indicate the extent of collaboration between organizations use co-authorship data in citation databases. Publication data from these databases are not publicly available and can be expensive to access and so hyperlink data has been proposed as an alternative. This paper investigates whether using machine learning methods to filter page types can improve the extent to which hyperlink data can be used to indicate the extent of collaboration between universities. Structured information about research projects extracted from UK and EU funding agency websites, co-authored publications and academic links between universities were analysed to identify if there is any association between the number of hyperlinks connecting two universities, with and without machine learning filtering, and the number of publications they co-authored. An increased correlation was found between the number of inlinks to a university's website and the extent to which it collaborates with other universities when machine learning techniques were used to filter out apparently irrelevant inlinks.