Conspiracy Spoofing Detection via Structure-Augmented Generative Graph Model

Abstract

Detecting spoofing in financial trading is a critical data mining task. While traditional machine learning models focus on individual node features, graph-based methodologies have shown superior performance by integrating relational data and structure information. However, spoofing transactions often exhibit distribution shifts, making historical data less effective. Instead, certain consistent trading patterns, such as motif structures, remain robust for analysis across different distributions. Therefore, this paper introduces the Structure-Augmented Generative Graph Model (SAG^2M) to detect conspiracy spoofing through substructure frequency-augmented detection. Our approach extracts subgraph pattern frequencies among neighboring nodes using an enumeration algorithm, and encodes these motif frequencies into a structure-augmented generative framework. A temporal and heterogeneous graph generation and aggregation scheme is then applied to collect neighborhood node information, effectively uncovering conspiracy spoofing patterns. Experiments on benchmark datasets demonstrate that SAG^2M outperforms existing models in detection accuracy. Case studies further highlight its superior effectiveness in identifying complex fraudulent behaviors.