The rapid evolution of the Ethereum network necessitates sophisticated techniques to ensure its robustness against potential threats and to maintain transparency. While Graph Neural Networks (GNNs) have pioneered anomaly detection in such platforms, capturing the intricacies of both spatial and temporal transactional patterns has remained a challenge. This study presents a fusion of Graph Convolutional Networks (GCNs) with Temporal Random Walks (TRW) enhanced by probabilistic sampling to bridge this gap. Our approach, unlike traditional GCNs, leverages the strengths of TRW to discern complex temporal sequences in Ethereum transactions, thereby providing a more nuanced transaction anomaly detection mechanism. Preliminary evaluations demonstrate that our TRW-GCN framework substantially advances the performance metrics over conventional GCNs in detecting anomalies and transaction bursts. This research not only underscores the potential of temporal cues in Ethereum transactional data but also offers a scalable and effective methodology for ensuring the security and transparency of decentralized platforms. By harnessing both spatial relationships and time-based transactional sequences as node features, our model introduces an additional layer of granularity, making the detection process more robust and less prone to false positives. This work lays the foundation for future research aimed at optimizing and enhancing the transparency of blockchain technologies, and serves as a testament to the significance of considering both time and space dimensions in the ever-evolving landscape of the decentralized platforms.

翻译：以太坊网络的快速发展需要复杂技术来确保其抵御潜在威胁的鲁棒性并维持透明度。尽管图神经网络（GNNs）已在平台异常检测领域取得开创性进展，但捕捉空间与时间交易模式的复杂性仍是挑战。本研究提出一种融合图卷积网络（GCNs）与概率采样增强时间随机游走（TRW）的框架以弥合这一鸿沟。不同于传统GCNs，我们的方法利用TRW的优势识别以太坊交易中复杂的时间序列模式，从而提供更精细的交易异常检测机制。初步评估表明，所提出的TRW-GCN框架在检测异常与交易爆发方面显著优化了传统GCNs的性能指标。本研究不仅揭示了以太坊交易数据中时间线索的潜力，还为保障去中心化平台的安全性与透明度提供了可扩展的有效方法论。通过将空间关系与基于时间的交易序列共同作为节点特征，我们的模型引入了额外的细粒度层次，使检测过程更鲁棒且不易产生误报。本工作为优化和提升区块链技术透明度的未来研究奠定了基础，并证明了在去中心化平台不断演进的过程中同时考虑时空维度的关键意义。