Ever-evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of addresses and diverse anomalous behaviors. Recently, advanced Graph Anomaly Detection (GAD) approaches applied to blockchains have faced two critical challenges: \textit{adversarial pattern evolution by malicious actors} and \textit{the out-of-distribution (OOD) problem caused by varied transaction semantics on blockchains}. To address these challenges, we propose a novel framework termed \textbf{TE}mporal \textbf{M}otif-aware \textbf{G}raph \textbf{T}est-\textbf{T}ime \textbf{A}daptation (\textbf{TEMG-TTA}). First, we comprehensively capture the 3-node temporal motif distribution of each active address using an efficient computational mechanism, enabling downstream temporal motif-aware graph learning. Second, we design a simple yet effective test-time adaptation strategy to facilitate the sharing of common patterns between training and testing graphs. Extensive experiments on 5 real-world datasets demonstrate that our proposed \textbf{TEMG-TTA} outperforms \textit{state-of-the-art} GAD approaches by an average of 54.88\%. A further case study on interpretable motif patterns reveals that \textbf{TEMG-TTA} explicitly characterizes the complex transaction patterns of anomalous addresses, thereby verifying the effectiveness of our technical designs. Our code will be made publicly available https://github.com/LuoXishuang0712/TEMG-TTA/.

翻译：不断演变的交易模式由于地址数量庞大及异常行为多样化，严重阻碍了新兴加密货币区块链上的异常检测。近期应用于区块链的先进图异常检测方法面临两个关键挑战：恶意行为者导致的对抗性模式演化，以及区块链上交易语义差异引发的分布外问题。为应对这些挑战，我们提出了一种名为时序图测试时自适应（TEMG-TTA）的新框架。首先，通过高效计算机制全面捕获每个活跃地址的三节点时序模体分布，实现下游的时序模体感知图学习。其次，我们设计了一种简洁而有效的测试时自适应策略，促进训练图与测试图之间通用模式的共享。在5个真实世界数据集上的大量实验表明，所提出的TEMG-TTA平均比现有最先进图异常检测方法高出54.88%。进一步的可解释模体模式案例研究表明，TEMG-TTA能明确刻画异常地址的复杂交易模式，从而验证了技术设计的有效性。我们的代码将开源至https://github.com/LuoXishuang0712/TEMG-TTA/。