Abstrak
Bid rigging and procurement collusion may produce structural patterns in the relationships between companies and procurement opportunities. This study compares Graph Convolutional Network (GCN) and GraphSAGE for structural anomaly detection in a heterogeneous company–tender procurement network. The study uses Government Procurement via GeBIZ data from Singapore and introduces controlled synthetic anomalies because the dataset does not provide complete bidder-participation information or verified collusion labels. A leakage-aware temporal evaluation is applied together with multiple anomaly difficulties, random seeds, unseen anomaly subjects, and cross-pattern testing. The results show different empirical behavior between the two architectures. GraphSAGE achieves higher F1-Score, ROC-AUC, and PR-AUC in the multi-seed evaluation, while GCN achieves higher recall. GraphSAGE also performs better in the true inductive evaluation on unseen anomaly subjects. An Early Warning System based on GraphSAGE is further developed to rank companies according to their structural anomaly scores. The findings indicate that graph-based models can support structural anomaly screening, while synthetic anomaly results should not be interpreted as evidence of confirmed procurement collusion.
Referensi
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