  Node classification is a substantial problem in graph-based fraud detection.
Many existing works adopt Graph Neural Networks (GNNs) to enhance fraud
detectors. While promising, currently most GNN-based fraud detectors fail to
generalize to the low homophily setting. Besides, label utilization has been
proved to be significant factor for node classification problem. But we find
they are less effective in fraud detection tasks due to the low homophily in
graphs. In this work, we propose GAGA, a novel Group AGgregation enhanced
TrAnsformer, to tackle the above challenges. Specifically, the group
aggregation provides a portable method to cope with the low homophily issue.
Such an aggregation explicitly integrates the label information to generate
distinguishable neighborhood information. Along with group aggregation, an
attempt towards end-to-end trainable group encoding is proposed which augments
the original feature space with the class labels. Meanwhile, we devise two
additional learnable encodings to recognize the structural and relational
context. Then, we combine the group aggregation and the learnable encodings
into a Transformer encoder to capture the semantic information. Experimental
results clearly show that GAGA outperforms other competitive graph-based fraud
detectors by up to 24.39% on two trending public datasets and a real-world
industrial dataset from Anonymous. Even more, the group aggregation is
demonstrated to outperform other label utilization methods (e.g., C&S,
BoT/UniMP) in the low homophily setting.
