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.