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BiRank: Fast and Flexible Ranking on Bipartite Networks with R and Python

Kai-Cheng Yang,B. Aronson,Yong-Yeol Ahn

Published 2020 in Journal of Open Source Software

ABSTRACT

Bipartite (two-mode) networks are ubiquitous. Common examples include networks of collaboration between scientists and their shared papers, networks of affiliation between corporate directors and board members, networks of patients and their doctors, and networks of competition between companies and their shared consumers. Bipartite networks are commonly reduced to unipartite networks for further analysis, such as calculating node centrality (e.g. PageRank, see Figure 1(c)). However, one-mode projections often destroy important structural information (Lehmann, Schwartz, & Hansen, 2008) and can lead to imprecise network measurements. Moreover, there are numerous ways to obtain unipartite networks from a bipartite network, each of which has different characteristics and idiosyncrasies (Bass et al., 2013).

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