  This paper presents the Global and Local Confidence Graph Neural Network
(GLC-GNN), an innovative approach to graph-based anomaly detection that
addresses the challenges of heterophily and camouflage in fraudulent
activities. By introducing a prototype to encapsulate the global features of a
graph and calculating a Global Confidence (GC) value for each node, GLC-GNN
effectively distinguishes between benign and fraudulent nodes. The model
leverages GC to generate attention values for message aggregation, enhancing
its ability to capture both homophily and heterophily. Through extensive
experiments on four open datasets, GLC-GNN demonstrates superior performance
over state-of-the-art models in accuracy and convergence speed, while
maintaining a compact model size and expedited training process. The
integration of global and local confidence measures in GLC-GNN offers a robust
solution for detecting anomalies in graphs, with significant implications for
fraud detection across diverse domains.
