对抗合成+自校正，让图神经网络更抗结构噪声
AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation
- 用对抗生成合成多尺度结构，提升初始表示鲁棒性
- 自适应注意力机制应对异质邻居，提升分类准确率
- 适合处理噪声大、非同质的复杂图数据场景
图神经网络在面对结构噪声或非同质拓扑时性能显著下降。为此，我们提出AdvSynGNN，一种面向节点级表征学习的鲁棒架构。该框架通过多分辨率结构合成与对比目标，建立对几何敏感的初始化；采用基于Transformer的主干网络，通过学习到的拓扑信号动态调节注意力机制，以适应异质性。核心贡献在于集成式对抗传播引擎：生成组件识别潜在连接变化，判别器维持全局一致性。此外，基于节点置信度的残差校正方案实现标签精炼，保障迭代稳定性。实验证明，该协同方法在多种图分布上有效提升预测精度，同时保持计算高效。研究还提供了实际部署协议，支持在大规模环境中稳健应用。
原文摘要 · Abstract (English)
Graph neural networks frequently encounter significant performance degradation when confronted with structural noise or non-homophilous topologies. To address these systemic vulnerabilities, we present AdvSynGNN, a comprehensive architecture designed for resilient node-level representation learning. The proposed framework orchestrates multi-resolution structural synthesis alongside contrastive objectives to establish geometry-sensitive initializations. We develop a transformer backbone that adaptively accommodates heterophily by modulating attention mechanisms through learned topological signals. Central to our contribution is an integrated adversarial propagation engine, where a generative component identifies potential connectivity alterations while a discriminator enforces global coherence. Furthermore, label refinement is achieved through a residual correction scheme guided by per-node confidence metrics, which facilitates precise control over iterative stability. Empirical evaluations demonstrate that this synergistic approach effectively optimizes predictive accuracy across diverse graph distributions while maintaining computational efficiency. The study concludes with practical implementation protocols to ensure the robust deployment of the AdvSynGNN system in large-scale environments.
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