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.

本研究通过将图卷积网络（GCNs）与时间随机游走（TRW）相结合，并借助概率抽样，来识别以太坊交易的复杂时间序列，从而提供更精细的交易异常检测机制。初步评估表明，我们的 TRW-GCN 框架在检测异常和交易突发事件方面显著提高了性能指标。此研究通过利用空间关系和基于时间的交易序列作为节点特征引入了更高层次的细粒度，使检测过程更健壮且不易产生误报，为未来优化和提高区块链技术透明度的研究奠定了基础。