Technological Convergence and Innovation Pathways in Sustainable Logistics Systems: An Integrated Graph Neural Network and Main Path Analysis

Abstract

The sustainable transformation of logistics and supply chains increasingly depends on the convergence of digital and physical technologies. However, prior studies have often analyzed these domains in isolation, lacking a unified model that captures both structural interdependence and temporal evolution in technological innovation. We develop an integrated model which combines a Variational Graph Autoencoder (VGAE) for structural embedding with Main Path Analysis (MPA) for tracing the temporal diffusion of technologies. Using 4121 patents published between 2015 and 2024 across 46 IPC subclasses, the model identifies four major innovation pathways—autonomous vehicle coordination, AI-driven logistics platforms, electrified mobility, and IoT-based monitoring that characterize the evolution of Logistics 4.0. The proposed model achieves a 62.5% main path contribution ratio, a weighted modularity (Q) of 0.5439, and a temporal alignment score of 0.51, confirming both structural coherence and interpretability. Empirical cross-validation with global policy reports (2024) and industry outlook assessments demonstrated strong consistency between the patent-based diffusion trajectories and real-world industry trends. The results provide actionable insights for policymakers and industry leaders seeking to align technological innovation with industrial infrastructure development and sustainable urban logistics transformation.