  Graph neural networks (GNN) have emerged as a powerful tool for fraud
detection tasks, where fraudulent nodes are identified by aggregating neighbor
information via different relations. To get around such detection, crafty
fraudsters resort to camouflage via connecting to legitimate users (i.e.,
relation camouflage) or providing seemingly legitimate feedbacks (i.e., feature
camouflage). A wide-spread solution reinforces the GNN aggregation process with
neighbor selectors according to original node features. This method may carry
limitations when identifying fraudsters not only with the relation camouflage,
but with the feature camouflage making them hard to distinguish from their
legitimate neighbors. In this paper, we propose a Hierarchical Attention-based
Graph Neural Network (HA-GNN) for fraud detection, which incorporates weighted
adjacency matrices across different relations against camouflage. This is
motivated in the Relational Density Theory and is exploited for forming a
hierarchical attention-based graph neural network. Specifically, we design a
relation attention module to reflect the tie strength between two nodes, while
a neighborhood attention module to capture the long-range structural affinity
associated with the graph. We generate node embeddings by aggregating
information from local/long-range structures and original node features.
Experiments on three real-world datasets demonstrate the effectiveness of our
model over the state-of-the-arts.
