Projection transformation-based link prediction for possible collaboration connections in bimodal supply chain networks

Abstract

Aiming at the problems of information disconnection and business interruption in supply chain networks caused by uncertainties such as trade wars and COVID-19, a projection-transformation-based model for predicting possible collaborative relationship links in bimodal supply chain networks is designed from the perspective of network science. The model first transforms the bimodal network structure of supply chain into a corresponding unimodal projection network by projection transformation; then screens the candidate collaborative links in the network; finally assigns weights to the similarity links according to different influencing factors, and calculates the possibility of potential collaborative links in the bimodal supply chain network by this. The experimental results show that the model can achieve higher quality prediction in real bimodal supply chain networks compared with other baseline link prediction models, which provides theoretical and methodological support for better formulation of business contingency plans and risk defense measures.