Papers › Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection
Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection
Yang Liu1, Xiang Ao, Zidi Qin, Jianfeng Chi, Jinghua Feng, Hao Yang, Qing He
Graph-based fraud detection approaches have escalated lots of attention recently due to the abundant relational information of graph-structured data, which may be beneficial for the detection of fraudsters. However, the GNN-based algorithms could fare poorly when the label distribution of nodes is heavily skewed, and it is common in sensitive areas such as financial fraud, etc. To remedy the class imbalance problem of graph-based fraud detection, we propose a Pick and Choose Graph Neural Network (PC-GNN for short) for imbalanced supervised learning on graphs. First, nodes and edges are picked with a devised label-balanced sampler to construct sub-graphs for mini-batch training. Next, for each node in the sub-graph, the neighbor candidates are chosen by a proposed neighborhood sampler. Finally, information from the selected neighbors and different relations are aggregated to obtain the final representation of a target node. Experiments on both benchmark and real-world graph-based fraud detection tasks demonstrate that PCGNN apparently outperforms state-of-the-art baselines.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Fraud Detection | Amazon-Fraud | PC-GNN | AUC-ROC | 95.86 | #5 of 6 | Archive leaderboard | report |
| Fraud Detection | Amazon-Fraud | PC-GNN | Averaged Precision | 85.49 | #5 of 6 | Archive leaderboard | report |
| Fraud Detection | Yelp-Fraud | PC-GNN | AUC-ROC | 79.87 | #9 of 10 | Archive leaderboard | report |
| Fraud Detection | Yelp-Fraud | PC-GNN | Averaged Precision | 48.10 | #9 of 10 | Archive leaderboard | report |
| Node Classification | Amazon-Fraud | PC-GNN | AUC-ROC | 95.86 | #5 of 6 | Archive leaderboard | report |
| Node Classification | Yelp-Fraud | PC-GNN | AUC-ROC | 79.87 | #8 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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