英文标题：
《Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional
  Networks for Financial Forensics》
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作者：
Mark Weber, Giacomo Domeniconi, Jie Chen, Daniel Karl I. Weidele,
  Claudio Bellei, Tom Robinson, Charles E. Leiserson
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最新提交年份：
2019
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英文摘要：
  Anti-money laundering (AML) regulations play a critical role in safeguarding financial systems, but bear high costs for institutions and drive financial exclusion for those on the socioeconomic and international margins. The advent of cryptocurrency has introduced an intriguing paradox: pseudonymity allows criminals to hide in plain sight, but open data gives more power to investigators and enables the crowdsourcing of forensic analysis. Meanwhile advances in learning algorithms show great promise for the AML toolkit. In this workshop tutorial, we motivate the opportunity to reconcile the cause of safety with that of financial inclusion. We contribute the Elliptic Data Set, a time series graph of over 200K Bitcoin transactions (nodes), 234K directed payment flows (edges), and 166 node features, including ones based on non-public data; to our knowledge, this is the largest labelled transaction data set publicly available in any cryptocurrency. We share results from a binary classification task predicting illicit transactions using variations of Logistic Regression (LR), Random Forest (RF), Multilayer Perceptrons (MLP), and Graph Convolutional Networks (GCN), with GCN being of special interest as an emergent new method for capturing relational information. The results show the superiority of Random Forest (RF), but also invite algorithmic work to combine the respective powers of RF and graph methods. Lastly, we consider visualization for analysis and explainability, which is difficult given the size and dynamism of real-world transaction graphs, and we offer a simple prototype capable of navigating the graph and observing model performance on illicit activity over time. With this tutorial and data set, we hope to a) invite feedback in support of our ongoing inquiry, and b) inspire others to work on this societally important challenge.
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中文摘要：
反洗钱（AML）法规在保障金融体系方面发挥着关键作用，但为机构带来了高昂的成本，并推动了社会经济和国际边缘群体的金融排斥。加密货币的出现带来了一个有趣的悖论：假名可以让罪犯隐藏在显而易见的地方，但开放数据为调查人员提供了更多的权力，并支持法医分析的众包。同时，学习算法的进步显示了AML工具包的巨大潜力。在本研讨会教程中，我们将利用这个机会来协调安全原因与金融包容性的原因。我们提供了椭圆数据集，这是一个时间序列图，包含超过20万个比特币交易（节点）、234K个定向支付流（边缘）和166个节点特征，包括基于非公开数据的特征；据我们所知，这是任何加密货币中公开的最大标记交易数据集。我们分享了使用逻辑回归（LR）、随机森林（RF）、多层感知器（MLP）和图卷积网络（GCN）预测非法交易的二元分类任务的结果，其中GCN是一种新兴的获取关系信息的新方法。结果显示了随机森林（RF）的优越性，但也要求算法工作将RF和图形方法各自的功能结合起来。最后，我们考虑可视化以进行分析和解释，鉴于真实世界交易图的大小和动态性，这很难实现，我们提供了一个简单的原型，能够导航图并观察模型在非法活动方面的性能。通过本教程和数据集，我们希望a）邀请反馈以支持我们正在进行的调查，b）鼓励其他人应对这一重要的社会挑战。
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Social and Information Networks        社会和信息网络
分类描述：Covers the design, analysis, and modeling of social and information networks, including their applications for on-line information access, communication, and interaction, and their roles as datasets in the exploration of questions in these and other domains, including connections to the social and biological sciences. Analysis and modeling of such networks includes topics in ACM Subject classes F.2, G.2, G.3, H.2, and I.2; applications in computing include topics in H.3, H.4, and H.5; and applications at the interface of computing and other disciplines include topics in J.1--J.7. Papers on computer communication systems and network protocols (e.g. TCP/IP) are generally a closer fit to the Networking and Internet Architecture (cs.NI) category.
涵盖社会和信息网络的设计、分析和建模，包括它们在联机信息访问、通信和交互方面的应用，以及它们作为数据集在这些领域和其他领域的问题探索中的作用，包括与社会和生物科学的联系。这类网络的分析和建模包括ACM学科类F.2、G.2、G.3、H.2和I.2的主题；计算应用包括H.3、H.4和H.5中的主题；计算和其他学科接口的应用程序包括J.1-J.7中的主题。关于计算机通信系统和网络协议（例如TCP/IP）的论文通常更适合网络和因特网体系结构（CS.NI）类别。
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一级分类：Computer Science        计算机科学
二级分类：Computers and Society        计算机与社会
分类描述：Covers impact of computers on society, computer ethics, information technology and public policy, legal aspects of computing, computers and education. Roughly includes material in ACM Subject Classes K.0, K.2, K.3, K.4, K.5, and K.7.
涵盖计算机对社会的影响、计算机伦理、信息技术和公共政策、计算机的法律方面、计算机和教育。大致包括ACM学科类K.0、K.2、K.3、K.4、K.5和K.7中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Quantitative Finance        数量金融学
二级分类：General Finance        一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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