LGAIOct 26, 2025

Enhancing Graph Classification Robustness with Singular Pooling

arXiv:2510.22643v11 citationsh-index: 8Has Code
Originality Highly original
AI Analysis

This work addresses the underexplored issue of adversarial robustness in graph classification for GNN users, offering a model-agnostic defense that is incremental but provides specific gains.

The paper tackles the problem of adversarial robustness in graph classification by analyzing the vulnerabilities of standard pooling methods and proposing a novel Robust Singular Pooling (RS-Pool) strategy, which shows improved robustness against attacks while maintaining competitive clean accuracy on real-world benchmarks.

Graph Neural Networks (GNNs) have achieved strong performance across a range of graph representation learning tasks, yet their adversarial robustness in graph classification remains underexplored compared to node classification. While most existing defenses focus on the message-passing component, this work investigates the overlooked role of pooling operations in shaping robustness. We present a theoretical analysis of standard flat pooling methods (sum, average and max), deriving upper bounds on their adversarial risk and identifying their vulnerabilities under different attack scenarios and graph structures. Motivated by these insights, we propose \textit{Robust Singular Pooling (RS-Pool)}, a novel pooling strategy that leverages the dominant singular vector of the node embedding matrix to construct a robust graph-level representation. We theoretically investigate the robustness of RS-Pool and interpret the resulting bound leading to improved understanding of our proposed pooling operator. While our analysis centers on Graph Convolutional Networks (GCNs), RS-Pool is model-agnostic and can be implemented efficiently via power iteration. Empirical results on real-world benchmarks show that RS-Pool provides better robustness than the considered pooling methods when subject to state-of-the-art adversarial attacks while maintaining competitive clean accuracy. Our code is publicly available at:\href{https://github.com/king/rs-pool}{https://github.com/king/rs-pool}.

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