用图神经网络分析区块链，提升安全与效率
Review of blockchain application with Graph Neural Networks, Graph Convolutional Networks and Convolutional Neural Networks
- 用GNN/GCN建模节点和交易关系，捕捉复杂关联
- CNN可处理结构化交易数据，发现时序空间模式
- 适合关注区块链安全与智能合约分析的研究者
本文综述了图神经网络（GNN）、图卷积网络（GCN）和卷积神经网络（CNN）在区块链技术中的应用。随着区块链网络的复杂性和采用率持续增长，传统分析方法难以捕捉去中心化系统的复杂关系与动态行为。为此，GNN、GCN和CNN等深度学习模型利用区块链固有的图结构和时序特性，提供强大解决方案。其中，GNN与GCN擅长建模节点与交易间的关联，适用于欺诈检测、交易验证与智能合约分析；而CNN可通过将数据表示为结构化矩阵，揭示交易流中的隐藏时序与空间模式。本文探讨这些模型如何提升线性区块链与有向无环图（DAG）系统在效率、安全性和可扩展性方面的表现，全面总结其优势与未来研究方向。通过融合先进神经网络技术，旨在展示其在革新区块链分析方面的潜力，推动更复杂的去中心化应用与网络性能优化。
原文摘要 · Abstract (English)
This paper reviews the applications of Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs), and Convolutional Neural Networks (CNNs) in blockchain technology. As the complexity and adoption of blockchain networks continue to grow, traditional analytical methods are proving inadequate in capturing the intricate relationships and dynamic behaviors of decentralized systems. To address these limitations, deep learning models such as GNNs, GCNs, and CNNs offer robust solutions by leveraging the unique graph-based and temporal structures inherent in blockchain architectures. GNNs and GCNs, in particular, excel in modeling the relational data of blockchain nodes and transactions, making them ideal for applications such as fraud detection, transaction verification, and smart contract analysis. Meanwhile, CNNs can be adapted to analyze blockchain data when represented as structured matrices, revealing hidden temporal and spatial patterns in transaction flows. This paper explores how these models enhance the efficiency, security, and scalability of both linear blockchains and Directed Acyclic Graph (DAG)-based systems, providing a comprehensive overview of their strengths and future research directions. By integrating advanced neural network techniques, we aim to demonstrate the potential of these models in revolutionizing blockchain analytics, paving the way for more sophisticated decentralized applications and improved network performance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。