We study illicit account detection on transaction networks of
cryptocurrencies that are increasi_testngly important in online financial
markets. The surge of illicit activities on cryptocurrencies has resulted in
billions of losses from normal users. Existing solutions either rely on tedious
feature engineering to get handcrafted features, or are inadequate to fully
utilize the rich semantics of cryptocurrency transaction data, and
consequently, yield sub-optimal performance. In this paper, we formulate the
illicit account detection problem as a classification task over directed
multigraphs with edge attributes, and present DIAM, a novel multi-graph neural
network model to effectively detect illicit accounts on large transaction
networks. First, DIAM includes an Edge2Seq module that automatically learns
effective node representations preserving intrinsic transaction patterns of
parallel edges, by considering both edge attributes and directed edge sequence
dependencies. Then utilizing the multigraph topology, DIAM employs a new
Multigraph Discrepancy (MGD) module with a well-designed message passing
mechanism to capture the discrepant features between normal and illicit nodes,
supported by an attention mechanism. Assembling all techniques, DIAM is trained
in an end-to-end manner. Extensive experiments, comparing against 14 existing
solutions on 4 large cryptocurrency datasets of Bitcoin and Ethereum,
demonstrate that DIAM consistently achieves the best performance to accurately
detect illicit accounts, while being efficient. For instance, on a Bitcoin
dataset with 20 million nodes and 203 million edges, DIAM achieves F1 score
96.55%, significantly higher than the F1 score 83.92% of the best competitor.

我们研究了在线金融市场中越来越重要的加密货币交易网络上的非法账户检测。我们提出了 DIAM，这是一个新颖的多图神经网络模型，旨在有效地检测大型交易网络上的非法账户。通过将边属性和有向边序列依赖考虑在内，DIAM 自动学习了有效的节点表示，并利用多图拓扑结构采用了新的多图差异（MGD）模块，通过设计良好的消息传递机制捕捉正常和非法节点之间的差异特征，从而在大量实验证明 DIAM 在准确检测非法账户方面始终表现最佳，同时具有高效性。

加密货币交易网络中用于非法账号检测的高效多图神经网络

Effective Multi-Graph Neural Networks for Illicit Account Detection on  Cryptocurrency Transaction Networks

Many real-world ubiquitous applications, such as parking recommendations and
air pollution monitoring, benefit significantly from accurate long-term
spatio-temporal forecasting (LSTF). LSTF makes use of long-term dependency
between spatial and temporal domains, contextual information, and inherent
pattern in the data. Recent studies have revealed the potential of multi-graph
neural networks (MGNNs) to improve prediction performance. However, existing
MGNN methods cannot be directly applied to LSTF due to several issues: the low
level of generality, insufficient use of contextual information, and the
imbalanced graph fusion approach. To address these issues, we construct new
graph models to represent the contextual information of each node and the
long-term spatio-temporal data dependency structure. To fuse the information
across multiple graphs, we propose a new dynamic multi-graph fusion module to
characterize the correlations of nodes within a graph and the nodes across
graphs via the spatial attention and graph attention mechanisms. Furthermore,
we introduce a trainable weight tensor to indicate the importance of each node
in different graphs. Extensive experiments on two large-scale datasets
demonstrate that our proposed approaches significantly improve the performance
of existing graph neural network models in LSTF prediction tasks.

本文研究多图神经网络在长期时空预测中的应用，提出了一种新的动态多图融合模块来描述不同图之间的节点相关性，并引入可训练权重张量来指示不同图中每个节点的重要性。实验证明，我们的方法在长期时空预测任务中显著提高了现有图神经网络模型的性能。