A Study on the History and Maritime Economic Structure of Yuegang in the Middle Ages Based on Graph Neural Networks
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Abstract
Understanding the evolution of large-scale interconnected systems requires effective modeling of dynamic topology, node interactions, and structural resilience, which are also fundamental issues in modern communication and propagation networks. To quantitatively investigate the historical maritime economy of Yuegang during the Ming Dynasty, this study constructs a heterogeneous trade graph integrating ports, commodities, regions, and institutional entities and employs an EvolveGCN-based graph neural network to capture its temporal evolution. Dynamic representation learning is combined with SEAL-based link prediction to reconstruct incomplete historical connections and reveal changes in network density, community structure, and node centrality across different developmental stages. The results indicate that Yuegang maintained a peak degree centrality of 0.93 and betweenness centrality of 0.95 during its prosperity, while network density increased from 0.18 to 0.44 before rapidly declining after institutional disruption. Community analysis further demonstrates that modular trade organization significantly enhanced regional economic integration and network robustness. Rather than serving solely as a historical analytical tool, the proposed graph modeling framework illustrates how dynamic topology evolution and connectivity degradation influence the stability of complex interaction networks, providing methodological insights for topology-aware routing, resilience analysis, and graph-based optimization in large-scale communication and propagation systems.
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