Probabilistic Sampling-Enhanced Temporal-Spatial GCN: A Scalable
  Framework for Transaction Anomaly Detection in Ethereum Networks
Stefan Kambiz Behfar Jon Crowcroft
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.