Association-Focused Path Aggregation for Graph Fraud Detection
Tian Qiu, Wenda Li, Yang Gao, Jie Lei, Tao Wang, Yi Gao, Mingli Song, Zunlei Feng
Fraudulent activities have caused substantial negative social impacts and are exhibiting emerging characteristics such as intelligence and industrialization, posing challenges of high-order interactions, intricate dependencies, and the sparse yet concealed nature of fraudulent entities. Existing mainstream fraud detectors are limited by their narrow "receptive fields", focusing only on the relations between entities and their neighbors while neglecting long-range structural associations hidden between entities. To address this issue, a novel fraud detector based on Graph Path Aggregation (GPA) is proposed. It operates through variable-length path sampling, behavior-associated path encoding, path interaction and aggregation, and aggregation-enhanced fraud detection. To further facilitate interpretable association analysis, we synthesize G-Internet, the first benchmark dataset in the field of internet fraud detection. Extensive experiments across datasets in multiple fraud scenarios demonstrate that the proposed GPA outperforms mainstream fraud detectors by up to +15\% in Average Precision (AP). Additionally, GPA exhibits enhanced robustness to noisy labels and offers excellent interpretability by uncovering implicit fraudulent patterns across broader contexts.