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(GCN)是一类用于处理可以表示为图的数据的人工神经网络。由于金融交易可以自然地构建成图,因此GCN广泛应用于金融行业,特别是金融欺诈检测。在本文中,我们关注加密货币信任网络中的欺诈检测。在文献中,大多数工作集中在静态网络上。而在本研究中,我们考虑加密货币网络的演变性质,并使用本地结构和平衡理论来指导训练过程。具体而言,我们计算模体矩阵以捕捉局部拓扑信息,然后在GCN聚合过程中使用它们。每个快照的生成嵌入是时间窗口内嵌入的加权平均值,其中权重是可学习的参数。由于信任网络在每个边缘上都被签名,因此平衡理论被用来指导训练过程。在bitcoin-alpha和bitcoin-otc数据集上的实验结果表明,所提出的模型优于文献中的模型。