  Graph-based fraud detection has heretofore received considerable attention.
Owning to the great success of Graph Neural Networks (GNNs), many approaches
adopting GNNs for fraud detection has been gaining momentum. However, most
existing methods are based on the strong inductive bias of homophily, which
indicates that the context neighbors tend to have same labels or similar
features. In real scenarios, fraudsters often engage in camouflage behaviors in
order to avoid detection system. Therefore, the homophilic assumption no longer
holds, which is known as the inconsistency problem. In this paper, we argue
that the performance degradation is mainly attributed to the inconsistency
between topology and attribute. To address this problem, we propose to
disentangle the fraud network into two views, each corresponding to topology
and attribute respectively. Then we propose a simple and effective method that
uses the attention mechanism to adaptively fuse two views which captures
data-specific preference. In addition, we further improve it by introducing
mutual information constraints for topology and attribute. To this end, we
propose a Disentangled Information Graph Neural Network (DIGNN) model, which
utilizes variational bounds to find an approximate solution to our proposed
optimization objective function. Extensive experiments demonstrate that our
model can significantly outperform stateof-the-art baselines on real-world
fraud detection datasets.
