用p-Laplacian提升GNN抗攻击能力，计算更高效。
Enhancing Robustness of Graph Neural Networks through p-Laplacian
- 基于加权p-Laplacian设计新框架，降低计算开销。
- 在真实数据集上验证，对抗攻击下仍保持高精度。
- 适合需要高效鲁棒GNN的工业应用，如推荐系统。
随着日常生活中数据量的增长，企业和各利益相关方需分析数据以实现更优预测。传统关系型数据虽提供诸多洞察，但随着计算能力提升及对实体间深层关系理解的需求增加，亟需新型技术。图数据分析已成为揭示复杂关系的有力工具，近年来图神经网络（GNN）在社交网络分析、推荐系统、药物发现等场景中表现卓越。然而，数据可能遭受训练阶段（投毒攻击）或测试阶段（逃避攻击）的对抗性干扰，从而误导GNN模型输出。因此，提升GNN的鲁棒性至关重要。现有鲁棒方法计算成本高，且在攻击强度增强时性能下降。本文提出一种基于加权p-Laplacian的高效框架pLAPGNN，显著提升GNN鲁棒性。在多个真实数据集上的实证评估证明了该方法的有效性与效率。
原文摘要 · Abstract (English)
With the increase of data in day-to-day life, businesses and different stakeholders need to analyze the data for better predictions. Traditionally, relational data has been a source of various insights, but with the increase in computational power and the need to understand deeper relationships between entities, the need to design new techniques has arisen. For this graph data analysis has become an extraordinary tool for understanding the data, which reveals more realistic and flexible modelling of complex relationships. Recently, Graph Neural Networks (GNNs) have shown great promise in various applications, such as social network analysis, recommendation systems, drug discovery, and more. However, many adversarial attacks can happen over the data, whether during training (poisoning attack) or during testing (evasion attack), which can adversely manipulate the desired outcome from the GNN model. Therefore, it is crucial to make the GNNs robust to such attacks. The existing robustness methods are computationally demanding and perform poorly when the intensity of attack increases. This paper presents a computationally efficient framework, namely, pLAPGNN, based on weighted p-Laplacian for making GNNs robust. Empirical evaluation on real datasets establishes the efficacy and efficiency of the proposed method.
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