Assessing the influence of bibliometric factors and organizational characteristics on the centrality degree of inter-university collaborative networks: a neural network approach

Abstract

Introduction. The centrality degree of a university collaborative network indicates how many other universities the given university has active collaborations with. The study analyses the centrality of university-level collaboration networks and aim to assess the influence of organizational characteristics and bibliometric factors of universities on the centrality degree. Method. This study used artificial neural networks, particularly a multilayer perceptron. The input variables included number of documents published, citations, size, type, and location of the university. Data was extracted from the census of institutions identified within the inter-university collaborative networks of Santander and Caldas in Colombia. A total of 154 universities comprises the dataset for the territory of Santander and 126 for Caldas. Results. The results indicated that bibliometric factors had a significant influence on the centrality degree of the networks. Organizational characteristics also had an influence, but to a lesser extent than bibliometric factors. Conclusion. The study found that the research output and impact are the most important factors in predicting the centrality degree of a university in a collaborative network. This suggests that policies to increase the research output and impact of a university are likely to result in a more central position in the network.