  Cryptocurrencies are rapidly expanding and becoming vital in digital
financial markets. However, the rise in cryptocurrency-related illicit
activities has led to significant losses for users. To protect the security of
these platforms, it is critical to identify illicit accounts effectively.
Current detection methods mainly depend on feature engineering or are
inadequate to leverage the complex information within cryptocurrency
transaction networks, resulting in suboptimal performance. In this paper, we
present DIAM, an effective method for detecting illicit accounts in
cryptocurrency transaction networks modeled by directed multi-graphs with
attributed edges. DIAM first features an Edge2Seq module that captures
intrinsic transaction patterns from parallel edges by considering edge
attributes and their directed sequences, to generate effective node
representations. Then in DIAM, we design a multigraph Discrepancy (MGD) module
with a tailored message passing mechanism to capture the discrepant features
between normal and illicit nodes over the multigraph topology, assisted by an
attention mechanism. DIAM integrates these techniques for end-to-end training
to detect illicit accounts from legitimate ones. Extensive experiments,
comparing against 15 existing solutions on 4 large cryptocurrency datasets of
Bitcoin and Ethereum, demonstrate that DIAM consistently outperforms others in
accurately identifying illicit accounts. For example, on a Bitcoin dataset with
20 million nodes and 203 million edges, DIAM attains an F1 score of 96.55%,
markedly surpassing the runner-up's score of 83.92%. The code is available at
https://github.com/TommyDzh/DIAM.
