Anomaly Detection Over Multi-Relational Graphs Using Graph Structure Learning and Multi-Scale Meta-Path Graph Aggregation

Graph Neural Networks (GNNs) have recently achieved remarkable success in various learning tasks involving graph-structured data. However, their application to multi-relational graph anomaly detection problems on real-world datasets presents several challenges that significantly hinder performance: the structural noise and inconsistencies inherent in real-world graph data, difficulties in aggregating information across multiple relation types, and the imbalanced class labels. To address these limitations, we introduce a graph structure learning layer designed to refine the original, noisy graph structure, enhancing the representation of node relationships. This enables our model to effectively handle inconsistencies and structural noise present in the original graph data with the augmented graph topology. To further exploit the semantic information embedded within the multi-relational graph data, we developed a multi-scale aggregation layer that facilitates information propagation and fusion through multi-relational levels, utilizing automatically and implicitly extracted meta-path graphs. Additionally, to handle the impact of imbalanced class labels, which often degrades the classification performance of GNN-based approaches, we introduce a score reweighting mechanism that balances probability scores between minority and majority classes. This strategy improves the model’s learning ability, leading to improved class distinction. Comprehensive experiments are conducted using two popular real-world review datasets to validate the effectiveness of our proposed model. Empirical results demonstrate that our approach consistently outperforms state-of-the-art baselines across three evaluation metrics, namely Average Precision, AUC and GMean.