Illegal related-party transactions (RPT) are federal felonies that pose a severe threat to the stability and integrity of modern financial systems. The increasing frequency of RPTs forms complex and dynamic networks. Existing temporal graph learning methods tend to treat entities as functionally homogeneous, ignoring the diverse and evolving structural roles of nodes. Role-based embedding methods model global structure by bridging same-role nodes, but their reliance on a unified mechanism for aggregation and evolution means they fail to distinguish the underlying logic of distinct interactions governed by structural roles. The limitations motivate us to develop a customized role-based strategy. It can also adapt to evolving RPT dynamics, thereby forming a continuous regulatory process to combat illegal activities. In this paper, we propose an innovative Role Perceptual Augmented Temporal Graph Network (RPATGN) for proactive RPT detection. We analyze the structural roles of nodes and employ a role-based message passing mechanism that adapts its aggregation strategy based on the roles of interacting nodes. We integrate a variational graph recurrent neural network, enhanced by temporal contextual attention, to explicitly model the dynamics of the roles and the overall network evolution. Extensive experiments on real-world financial datasets demonstrate the effectiveness of our approach for RPT detection. It holds practical significance for fostering robust financial systems and promoting healthy, transparent financial markets.

Graph Neural Networks (GNNs) have achieved remarkable success in machine learning tasks by learning the features of graph data. However, experiments show that vanilla GNNs fail to achieve good classification performance in the field of graph anomaly detection. To address this issue, we propose and theoretically prove that the high-Class Homophily Variance (CHV) characteristic is the reason behind the suboptimal performance of GNN models in anomaly detection tasks. Statistical analysis shows that in most standard node classification datasets, homophily levels are similar across all classes, so CHV is low. In contrast, graph anomaly detection datasets have high CHV, as benign nodes are highly homophilic while anomalies are not, leading to a clear separation. To mitigate its impact, we propose a novel GNN model named Homophily Edge Augment Graph Neural Network (HEAug). Different from previous work, our method emphasizes generating new edges with low CHV value, using the original edges as an auxiliary. HEAug samples homophily adjacency matrices from scratch using a self-attention mechanism, and leverages nodes that are relevant in the feature space but not directly connected in the original graph. Additionally, we modify the loss function to punish the generation of unnecessary heterophilic edges by the model. Extensive comparison experiments demonstrate that HEAug achieved the best performance across eight benchmark datasets, including anomaly detection, edgeless node classification and adversarial attack. We also defined a heterophily attack to increase the CHV value in other graphs, demonstrating the effectiveness of our theory and model in various scenarios.

Spoofing is a deceptive trading strategy where fraudsters place a large number of fake orders to manipulate market prices, severely distorting market fairness and threatening market stability.With the advancement of fraudulent tactics, spoofing patterns span across various levels of interaction, involving not only the local structure of individual spoofing transactions but also spoofing groups and global patterns.Relying solely on local context makes it challenging to capture multi-granularity risk signals, especially for organized and covert spoofing.Additionally, existing methods fail to consider the differences and relative importance between features of varying granularity, leading to feature distortion and noise.Therefore, we propose a multi-granularity augmented graph learning method that differentially captures fraud signals at local, group, and global levels.It utilizes multi-hop differential aggregation and communityaugmented strategy to capture information from local to global perspectives, adaptively distinguishing the contributions of different granularity.To avoid excessive fusion of multi-granularity information, we combine contrastive loss and cross-entropy loss for joint optimization, preserving key features while enhancing the method's robustness and accuracy.Extensive experiments on real-world datasets demonstrate the effectiveness of our proposed approach in spoofing detection, providing a robust solution for regulatory agencies.Our work will help financial institutions enhance their regulatory capabilities, protect investors' interests, and promote the healthy development of financial markets.