Abstract
Fraud detection that aims to discern frauds from the majority of benigns has become an increasingly prominent research field. Recently, Graph Neural Networks (GNNs) have been widely applied in graph-based fraud detection due to their outstanding data analysis and mining capabilities. However, owing to the inherent homophily-heterophily mixture and class imbalance of fraud graphs, most GNNs with homophily assumption inevitably suffer from local abnormal signal loss during information propagation, posing significant challenges in situations where frauds are rare and valuable. To address the aforementioned issues, we present a novel adaptive node-subgraph contrastive learning approach for graph-based fraud detection, dubbed ANS-GFD. Specifically, we first design a node abnormality estimation module from the perspective of feature, which analyses the likelihood of a node belonging to fraud or benign by comparing the feature similarity between the target node and its corresponding subgraph. We then present a dynamic neighborhood modeling mechanism guided by the abnormal probability of a node to adaptively group and aggregate neighborhood information. By this means, the target node can effectively aggregate the neighbor information from the perspective of fraud or benign, thereby preserving as much fraud characteristics that occupy minority population as possible. Extensive experiments across four real-world fraud detection datasets demonstrate the superiority and effectiveness of our proposed ANS-GFD over state-of-the-art baselines.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
"Adaptive Node-Subgraph Contrastive Learning for Heterophilic Graph Fraud Detection", Technical Disclosure Commons, (May 04, 2025)
https://www.tdcommons.org/dpubs_series/8083