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.

翻译：隐形链的匿名加快了隐蔽货币平台非法活动和犯罪行为的增加。虽然权力下放是隐蔽货币平台的典型特征之一,但我们紧急呼吁进行有效监管,以发现这些非法行为,确保用户交易的安全和稳定。身份推断旨在对账户身份进行初步推断,在隐蔽链安全方面起着重要作用。作为一个共同工具,图形采矿技术可以有效地代表账户之间的交互信息,并用于身份推断。然而,现有方法无法平衡可缩放性和端至端结构,导致高计算消耗率和特征代表薄弱。在本文件中,我们提出了一个从交易子图角度分析用户行为的新办法,从交易子图的角度分析用户行为,这自然将身份推断任务转化为图表分类模式,并有效避免在大比例图中进行计算。此外,我们提议了一个通用端至端图神经网络模型,名为 $\ text{I ⁇ 2\ text{BNNNN}, 该模型可以接受子图作为投入,并学习对交易子图绘制交易子图的功能,以了解身份为账户特征,实现EG系统数据化,在数据库中实现E-greal化。