Predictive Characteristics of Co-authorship Networks: Comparing the Unweighted, Weighted, and Bipartite Cases

Abstract

Abstract Purpose This study aims to answer the question to what extent different types of networks can be used to predict future co-authorship among authors. Design/methodology/approach We compare three types of networks: unweighted networks, in which a link represents a past collaboration; weighted networks, in which links are weighted by the number of joint publications; and bipartite author-publication networks. The analysis investigates their relation to positive stability, as well as their potential in predicting links in future versions of the co-authorship network. Several hypotheses are tested. Findings Among other results, we find that weighted networks do not automatically lead to better predictions. Bipartite networks, however, outperform unweighted networks in almost all cases. Research limitations Only two relatively small case studies are considered. Practical implications The study suggests that future link prediction studies on co-occurrence networks should consider using the bipartite network as a training network. Originality/value This is the first systematic comparison of unweighted, weighted, and bipartite training networks in link prediction.