这篇论文发了TSAI-MetaFraud数据集，把行为、交易和图数据合在一起，专门做元界欺诈检测，搞多模态研究的可以看看。
TSAI-MetaFraud是一个整合行为、交易和图结构信息的多模态基准数据集，专为元界虚拟经济中的欺诈分析而设计。该数据集包含基于交易的欺诈检测、跨模态节点分类、时间链接预测和弱监督欺诈检测四个任务。研究人员使用机器学习模型和图神经网络提供了基线评估结果。它旨在推动多模态学习、图挖掘和可信AI在元界生态系统中的发展。
TSAI-MetaFraud: A Benchmark Dataset for Financial Fraud Transaction and Behavioral Risk Detection in Metaverse Ecosystems
The emergence of metaverse platforms has created virtual economies that introduce new challenges related to fraud, bot activity, and illicit financial behavior. Despite growing interest in trustworthy metaverse analytics, existing datasets typically focus on user behavior, authentication, or financial transactions in isolation, limiting the development and reproducible evaluation of multimodal fraud detection methods. To address this gap, we present TSAI-MetaFraud, a multimodal, multi-task benchmark dataset for fraud analytics in virtual economies. TSAI-MetaFraud integrates behavioral, transactional, and graph-structured information while incorporating realistic fraud and automated bot scenarios. We define benchmark tasks including transaction fraud detection, cross-modal node classification, temporal link prediction, and weakly supervised fraud detection, and provide baseline evaluations using machine learning models and graph neural networks. By jointly capturing behavioral activity, financial interactions, and relational structure within a unified virtual economy, TSAI-MetaFraud provides a benchmark for advancing multimodal learning, graph mining, fraud analytics, and trustworthy AI in emerging metaverse ecosystems.