Identifying missing and spurious connections via the bi-directional diffusion on bipartite networks

Abstract

Link prediction and spurious link detection in complex networks have attracted increasing attention from both physical and computer science communities, due to their wide applications in many real systems. Related previous works mainly focus on monopartite networks while these problems in bipartite networks are not yet systematically addressed. Containing two different kinds of nodes, bipartite networks are essentially different from monopartite networks, especially in node similarity calculation: the similarity between nodes of different kinds (called inter-similarity) is not well defined. In this letter, we employ the local diffusion processes to measure the inter-similarity in bipartite networks. We find that the inter-similarity is asymmetric if the diffusion is applied in different directions. Accordingly, we propose a bi-directional hybrid diffusion method which is shown to achieve higher accuracy than the existing diffusion methods in identifying missing and spurious links in bipartite networks. • We employ the local diffusion processes to measure the inter-similarity in bipartite networks. • The inter-similarity is asymmetric if the diffusion is applied in different directions. • We propose a bi-directional hybrid diffusion method. • Our method achieves high accuracy in identifying missing and spurious links.