Predictive Characteristics of Co-authorship Networks: Comparing the unweighted, weighted, and bipartite cases

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.