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<p>In the rapidly evolving landscape of blockchain technology, accurately classifying blockchain addresses is crucial for enhancing security, tracking illicit activities, and understanding transaction behaviors. This paper presents a novel classification method leveraging a Graph Neural Network (GNN) framework, which utilizes an account balance model to analyze relationships and transactions between blockchain addresses. By constructing a graph representation of the blockchain network, we capture both the structural and functional attributes of addresses. Our approach demonstrates significant improvements in classification accuracy compared to traditional methods, offering a robust solution for identifying various address types, including exchanges, personal wallets, and service providers. Experimental results show that our model effectively distinguishes between different classes of addresses, thereby contributing to the broader field of blockchain analytics and security.</p>

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Blockchain address classification using graph neural networks for enhanced security and analytics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月24日

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  • Vinay Kumar Kasula,
  • Akhila Reddy Yadulla,
  • Mounica Yenugula,
  • Bhargavi Konda,
  • Supraja Ayyamgari

摘要

In the rapidly evolving landscape of blockchain technology, accurately classifying blockchain addresses is crucial for enhancing security, tracking illicit activities, and understanding transaction behaviors. This paper presents a novel classification method leveraging a Graph Neural Network (GNN) framework, which utilizes an account balance model to analyze relationships and transactions between blockchain addresses. By constructing a graph representation of the blockchain network, we capture both the structural and functional attributes of addresses. Our approach demonstrates significant improvements in classification accuracy compared to traditional methods, offering a robust solution for identifying various address types, including exchanges, personal wallets, and service providers. Experimental results show that our model effectively distinguishes between different classes of addresses, thereby contributing to the broader field of blockchain analytics and security.