Advancing Graph Neural Networks for Complex Relational Learning: A Multi-Scale Heterogeneity-Aware Framework with Adversarial Robustness and Interpretable Analysis

Graph Neural Networks (GNNs) face fundamental algorithmic challenges in real-world applications due to a combination of data heterogeneity, adversarial heterophily, and severe class imbalance. A critical research gap exists for a unified framework that can simultaneously address these issues, limiting the deployment of GNNs in high-stakes domains like financial fraud detection and social network analysis. This paper introduces HAG-CFNet, a novel framework designed to bridge this gap by integrating three key innovations: (1) a heterogeneity-aware message-passing mechanism that uses relation-specific attention to capture rich semantic information; (2) a dual-channel heterophily detection module that explicitly identifies and neutralizes adversarial camouflage through separate aggregation pathways; and (3) a domain-aware counterfactual generator that produces plausible, actionable explanations by co-optimizing feature and structural perturbations. These are supported by a synergistic imbalance correction strategy combining graph-adapted oversampling with cost-sensitive learning. Extensive testing on large-scale financial datasets validates the framework’s impact: HAG-CFNet achieves a 4.2% AUC-PR improvement over state-of-the-art methods, demonstrates superior robustness by reducing performance degradation under structural noise by over 50%, and generates counterfactual explanations with 91.8% validity while requiring minimal perturbations. These advances provide a direct pathway to building more trustworthy and effective AI systems for critical applications ranging from financial risk management to supply chain analysis and social media content moderation.