基于图神经网络的区块链身份推断
Identity Inference on Blockchain using Graph Neural Network
摘要：
区块链的匿名性加速了加密货币平台上非法活动和犯罪行为的增长。尽管去中心化是区块链的典型特征之一，但我们迫切呼吁有效的监管来检测这些非法行为，以确保用户交易的安全和稳定。身份推断是对账户身份进行初步推断，在区块链安全中起着重要作用。图挖掘技术作为一种常用的工具，可以有效地表示账户之间的交互信息，并用于身份推理。然而，现有的方法不能平衡的可扩展性和端到端的架构，导致高的计算消耗和弱的特征表示。在本文中，我们提出了一种新的方法来分析用户的行为从交易子图的角度来看，自然转换到一个图分类模式的身份推理任务，有效地避免了在大规模图的计算。在此基础上，提出了一种通用的端到端图神经网络模型，命名为$\text{I}^2 \text{BGNN}$，该模型可以接受子图作为输入，并学习一个将交易子图模式映射到账户身份的函数，实现去匿名化。在EOSG和ETHG数据集上的大量实验表明，该方法在身份推理方面达到了最先进的性能。
英文摘要：
The anonymity of blockchain has accelerated the growth of illegal activities and criminal behaviors on cryptocurrency platforms. Although decentralization is one of the typical characteristics of blockchain, we urgently call for effective regulation to detect these illegal behaviors to ensure the safety and stability of user transactions. Identity inference, which aims to make a preliminary inference about account identity, plays a significant role in blockchain security. As a common tool, graph mining technique can effectively represent the interactive information between accounts and be used for identity inference. However, existing methods cannot balance scalability and end-to-end architecture, resulting high computational consumption and weak feature representation. In this paper, we present a novel approach to analyze user's behavior from the perspective of the transaction subgraph, which naturally transforms the identity inference task into a graph classification pattern and effectively avoids computation in large-scale graph. Furthermore, we propose a generic end-to-end graph neural network model, named $\text{I}^2 \text{BGNN}$, which can accept subgraph as input and learn a function mapping the transaction subgraph pattern to account identity, achieving de-anonymization. Extensive experiments on EOSG and ETHG datasets demonstrate that the proposed method achieve the state-of-the-art performance in identity inference.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
