  The graph-based model can help to detect suspicious fraud online. Owing to
the development of Graph Neural Networks~(GNNs), prior research work has
proposed many GNN-based fraud detection frameworks based on either homogeneous
graphs or heterogeneous graphs. These work follow the existing GNN framework by
aggregating the neighboring information to learn the node embedding, which lays
on the assumption that the neighbors share similar context, features, and
relations. However, the inconsistency problem is hardly investigated, i.e., the
context inconsistency, feature inconsistency, and relation inconsistency. In
this paper, we introduce these inconsistencies and design a new GNN framework,
$\mathsf{GraphConsis}$, to tackle the inconsistency problem: (1) for the
context inconsistency, we propose to combine the context embeddings with node
features, (2) for the feature inconsistency, we design a consistency score to
filter the inconsistent neighbors and generate corresponding sampling
probability, and (3) for the relation inconsistency, we learn a relation
attention weights associated with the sampled nodes. Empirical analysis on four
datasets indicates the inconsistency problem is crucial in a fraud detection
task. The extensive experiments prove the effectiveness of
$\mathsf{GraphConsis}$. We also released a GNN-based fraud detection toolbox
with implementations of SOTA models. The code is available at
https://github.com/safe-graph/DGFraud.
