  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.
