Subgraph representation learning is a technique for analyzing local
structures (or shapes) within complex networks. Enabled by recent developments
in scalable Graph Neural Networks (GNNs), this approach encodes relational
information at a subgroup level (multiple connected nodes) rather than at a
node level of abstraction. We posit that certain domain applications, such as
anti-money laundering (AML), are inherently subgraph problems and mainstream
graph techniques have been operating at a suboptimal level of abstraction. This
is due in part to the scarcity of annotated datasets of real-world size and
complexity, as well as the lack of software tools for managing subgraph GNN
workflows at scale. To enable work in fundamental algorithms as well as domain
applications in AML and beyond, we introduce Elliptic2, a large graph dataset
containing 122K labeled subgraphs of Bitcoin clusters within a background graph
consisting of 49M node clusters and 196M edge transactions. The dataset
provides subgraphs known to be linked to illicit activity for learning the set
of "shapes" that money laundering exhibits in cryptocurrency and accurately
classifying new criminal activity. Along with the dataset we share our graph
techniques, software tooling, promising early experimental results, and new
domain insights already gleaned from this approach. Taken together, we find
immediate practical value in this approach and the potential for a new standard
in anti-money laundering and forensic analytics in cryptocurrencies and other
financial networks.

通过使用可扩展的图神经网络，以子图级别编码关系信息，通过提供具有反洗钱犯罪活动相关子图的数据集，实现对加密货币中的洗钱行为进行形状学习和准确分类，为反洗钱和取证分析提供了潜在的新标准和直接实际价值。

洗钱的形状：使用 Elliptic2 数据集上区块链的子图表示学习

The Shape of Money Laundering: Subgraph Representation Learning on the  Blockchain with the Elliptic2 Dataset

Subgraph representation learning based on Graph Neural Network (GNN) has
exhibited broad applications in scientific advancements, such as predictions of
molecular structure-property relationships and collective cellular function. In
particular, graph augmentation techniques have shown promising results in
improving graph-based and node-based classification tasks. Still, they have
rarely been explored in the existing GNN-based subgraph representation learning
studies. In this study, we develop a novel multi-view augmentation mechanism to
improve subgraph representation learning models and thus the accuracy of
downstream prediction tasks. Our augmentation technique creates multiple
variants of subgraphs and embeds these variants into the original graph to
achieve highly improved training efficiency, scalability, and accuracy.
Benchmark experiments on several real-world biological and physiological
datasets demonstrate the superiority of our proposed multi-view augmentation
techniques in subgraph representation learning.

本研究提出一种新颖的多视角扩充机制，以改进基于子图表征学习模型的训练效果和下游预测任务的准确性，在多个真实世界的生物和生理数据集上的基准实验表明，该扩充技术相较于现有的图神经网络表征学习研究具有更好的训练效率、可扩展性和准确度。