Graph convolutional networks (GCNs) is a class of artificial neural networks for processing data that can be represented as graphs. Since financial transactions can naturally be constructed as graphs, GCNs are widely applied in the financial industry, especially for financial fraud detection. In this paper, we focus on fraud detection on cryptocurrency truct networks. In the literature, most works focus on static networks. Whereas in this study, we consider the evolving nature of cryptocurrency networks, and use local structural as well as the balance theory to guide the training process. More specifically, we compute motif matrices to capture the local topological information, then use them in the GCN aggregation process. The generated embedding at each snapshot is a weighted average of embeddings within a time window, where the weights are learnable parameters. Since the trust networks is signed on each edge, balance theory is used to guide the training process. Experimental results on bitcoin-alpha and bitcoin-otc datasets show that the proposed model outperforms those in the literature.

翻译：图卷积网络是一类能够处理图结构数据的人工神经网络。由于金融交易天然可构建为图结构，图卷积网络在金融领域得到广泛应用，尤其是在金融欺诈检测中。本文聚焦于加密货币信任网络中的欺诈检测问题。现有文献多关注静态网络，而本研究考虑了加密货币网络随时间演化的特性，并采用局部结构与平衡理论指导训练过程。具体而言，我们通过计算动机矩阵来捕获局部拓扑信息，并将其应用于图卷积网络的聚合过程中。每个时间快照生成的嵌入向量是其时间窗口内嵌入向量的加权平均，其中权重为可学习参数。由于信任网络每条边均带有符号标记，我们利用平衡理论指导训练过程。在bitcoin-alpha和bitcoin-otc数据集上的实验结果表明，所提模型性能优于现有文献方法。