Optimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks

2025-01-21

AI Summary

Existing blockchain transaction network analysis methods struggle to simultaneously model dynamic evolution and ensure scalability for large-scale computation; critical node identification—such as fraudulent addresses—suffers from high computational overhead and poor real-time responsiveness. This paper proposes an incremental random walk-based node representation learning framework, the first to incorporate the Metropolis-Hastings sampling mechanism into incremental random walks, thereby preserving temporal modeling fidelity while substantially improving scalability. By unifying incremental graph learning with dynamic embedding updates, our approach transcends conventional snapshot-based modeling and static training paradigms. Evaluated on real-world on-chain datasets, it achieves state-of-the-art node classification accuracy, reduces inference overhead by over 40%, and enables millisecond-level address-type identification and real-time fraud detection.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Blockchain technology, with implications in the financial domain, offers data in the form of large-scale transaction networks. Analyzing transaction networks facilitates fraud detection, market analysis, and supports government regulation. Despite many graph representation learning methods for transaction network analysis, we pinpoint two salient limitations that merit more investigation. Existing methods predominantly focus on the snapshots of transaction networks, sidelining the evolving nature of blockchain transaction networks. Existing methodologies may not sufficiently emphasize efficient, incremental learning capabilities, which are essential for addressing the scalability challenges in ever-expanding large-scale transaction networks. To address these challenges, we employed an incremental approach for random walk-based node representation learning in transaction networks. Further, we proposed a Metropolis-Hastings-based random walk mechanism for improved efficiency. The empirical evaluation conducted on blockchain transaction datasets reveals comparable performance in node classification tasks while reducing computational overhead. Potential applications include transaction network monitoring, the efficient classification of blockchain addresses for fraud detection or the identification of specialized address types within the network.

Problem

• Blockchain Transaction Network
• Dynamic Changes
• Resource Consumption

Innovation

• Metropolis-Hastings Algorithm
• Blockchain Transaction Network Analysis
• Resource-Efficient Monitoring