I. Introduction
Anomalies (aka. fraudsters) delineate the abnormal objects that deviate significantly from the normal (aka. benign) [1]. This issue has garnered considerable attention in various domains, including distinguishing fake reviews [2] or misinformation in social networks [3], [4], and detecting fraudulent behavior in financial transactions [5]. Generally, abnormal and normal objects are intricately connected through complex relationships, which can be effectively represented as graphs [2]. Wherein, nodes represent these objects, and edges interpret their relationships. To address the GAD problem on such structural data, many state-of-the-art methods [2], [5], [6], [7] adopt a semi-supervised node classification approach, where only a subset of nodes is labeled as training data, while the remainder is employed as the testing data. To distill the discriminative information for the hidden anomalies, these methods mostly apply graph neural networks (GNNs) [8], [9], [10] that propagate the label-aware signals along with the graph structure.