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

Scalable graph neural networks (GNNs) have emerged as a promising technique,
which exhibits superior predictive performance and high running efficiency
across numerous large-scale graph-based web applications. However, (i) Most
scalable GNNs tend to treat all nodes in graphs with the same propagation
rules, neglecting their topological uniqueness; (ii) Existing node-wise
propagation optimization strategies are insufficient on web-scale graphs with
intricate topology, where a full portrayal of nodes' local properties is
required. Intuitively, different nodes in web-scale graphs possess distinct
topological roles, and therefore propagating them indiscriminately or neglect
local contexts may compromise the quality of node representations. This
intricate topology in web-scale graphs cannot be matched by small-scale
scenarios. To address the above issues, we propose \textbf{A}daptive
\textbf{T}opology-aware \textbf{P}ropagation (ATP), which reduces potential
high-bias propagation and extracts structural patterns of each node in a
scalable manner to improve running efficiency and predictive performance.
Remarkably, ATP is crafted to be a plug-and-play node-wise propagation
optimization strategy, allowing for offline execution independent of the graph
learning process in a new perspective. Therefore, this approach can be
seamlessly integrated into most scalable GNNs while remain orthogonal to
existing node-wise propagation optimization strategies. Extensive experiments
on 12 datasets, including the most representative large-scale ogbn-papers100M,
have demonstrated the effectiveness of ATP. Specifically, ATP has proven to be
efficient in improving the performance of prevalent scalable GNNs for
semi-supervised node classification while addressing redundant computational
costs.

基于可扩展图神经网络的自适应拓扑感知传播 (ATP) 提高了运行效率和预测性能，减少了潜在的高偏差传播，并以可插入式的节点传播优化策略无缝集成于大规模图神经网络，同时解决了冗余计算成本。