Common neighbours and the local-community-paradigm for link prediction in bipartite networks

TL;DR

This paper introduces a formal framework for local-based link prediction in bipartite networks, overcoming theoretical limitations and demonstrating improved prediction accuracy across various real-world systems.

Contribution

It provides the first formal definitions of common neighbour index and local-community-paradigm for bipartite networks, enabling effective local link prediction models.

Findings

Models significantly improve link prediction accuracy in bipartite networks.

The framework exploits local physical forces influencing network organization.

Effective across technological, social, and biological systems.

Abstract

Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index (CN) and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively implement neighbourhood-based link prediction entirely in the bipartite domain.