The Message Passing Mechanism of Graph Neural Networks and Its Potential for Processing Structured Data

Abstract

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Graph neural networks (GNNs) achieve deep representation learning of graph-structured data through a message passing mechanism, becoming a core technical paradigm for processing structured data. This paper systematically analyzes the architectural principles of the message passing mechanism, illustrating its three-step core process of "message generation-aggregation-update" and its mathematical implementation. The paper focuses on comparing the mechanism with typical models such as GCN and GAT. Drawing on empirical examples from fields such as bioinformatics, financial risk control, and retail supply chain, the paper reveals the unique advantages of this mechanism in capturing entity associations and discovering hidden patterns. In protein function prediction, the message passing-based DeepFRI model achieves accurate mapping between structure and function; in financial fraud detection, aggregating multi-hop transaction information significantly improves the ability to identify hidden criminal networks. The paper also analyzes current challenges such as oversmoothing and large-scale graph adaptation, proposing solutions such as residual connection optimization and neighbor sampling. This research demonstrates that the message passing mechanism provides a modeling approach that captures the relational nature of structured data, demonstrating strong potential for application in complex, cross-domain problems. Future applications will be further overcome by combining self-supervised learning with large-scale model technologies.