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.

翻译：随着日常生活中的数据日益增多，企业和不同利益相关者需要分析数据以做出更准确的预测。传统上，关系型数据一直是各类洞察的重要来源，但随着计算能力的提升以及对实体间深层关系理解需求的增长，设计新技术的需求应运而生。为此，图数据分析已成为理解数据的卓越工具，能够对复杂关系进行更现实、更灵活的建模。近年来，图神经网络（GNNs）在社交网络分析、推荐系统、药物发现等诸多应用中展现出巨大潜力。然而，数据可能遭受多种对抗性攻击，无论是在训练阶段（投毒攻击）还是在测试阶段（规避攻击），这些攻击都可能恶意篡改GNN模型的预期输出。因此，提升GNN对此类攻击的鲁棒性至关重要。现有的鲁棒性方法计算成本高昂，且在攻击强度增加时表现不佳。本文提出一种基于加权p-拉普拉斯算子的计算高效框架——pLapGNN，用以增强GNN的鲁棒性。在真实数据集上的实证评估验证了所提方法的有效性与高效性。