Motif-based link prediction in multiplex networks: A method incorporating evolutionary multilateral patterns for trade flow estimation

In the complex systems of supply chains, capturing the dynamic nature of multilateral interactions remains a key challenge for link prediction. Traditional approaches often rely on static or dyadic representations, which may overlook evolving higher-order structural patterns. This study proposes a multiplex network link prediction framework that integrates time-evolving multilateral trade patterns explored by network motifs to estimate potential trade flows. Applied to a high-purity quartz supply chain network, which is strategically important to high-tech industries, the method achieves up to 3.6% higher prediction performance compared to baseline models. We demonstrate that motif-based features significantly enhance prediction accuracy by encoding meso-scale evolution of multilateral trade patterns over time and across network layers. Results further reveal distinct roles of countries within the multiplex structure, with the United States, Germany, China, and Japan exhibiting layer-specific dominance. In addition, the United States will lead upstream potential trade, with Eurasia strengthening midstream and downstream trade ties. The proposed approach offers a generalizable methodology for link prediction in evolving multiplex networks, and is helpful for policy guidance of supply chains.