Graph Neural Networks (GNNs) are gaining popularity across various domains
due to their effectiveness in learning graph-structured data. Nevertheless,
they have been shown to be susceptible to backdoor poisoning attacks, which
pose serious threats to real-world applications. Meanwhile, graph reduction
techniques, including coarsening and sparsification, which have long been
employed to improve the scalability of large graph computational tasks, have
recently emerged as effective methods for accelerating GNN training on
large-scale graphs. However, the current development and deployment of graph
reduction techniques for large graphs overlook the potential risks of data
poisoning attacks against GNNs. It is not yet clear how graph reduction
interacts with existing backdoor attacks. This paper conducts a thorough
examination of the robustness of graph reduction methods in scalable GNN
training in the presence of state-of-the-art backdoor attacks. We performed a
comprehensive robustness analysis across six coarsening methods and six
sparsification methods for graph reduction, under three GNN backdoor attacks
against three GNN architectures. Our findings indicate that the effectiveness
of graph reduction methods in mitigating attack success rates varies
significantly, with some methods even exacerbating the attacks. Through
detailed analyses of triggers and poisoned nodes, we interpret our findings and
enhance our understanding of how graph reduction interacts with backdoor
attacks. These results highlight the critical need for incorporating robustness
considerations in graph reduction for GNN training, ensuring that enhancements
in computational efficiency do not compromise the security of GNN systems.

本文通过对六种粗化方法和六种稀疏化方法的综合鲁棒性分析，研究了图结构规模的 GN 任务中图规约方法在最新的后门攻击下的鲁棒性，结果表明鲁棒性因方法而异，一些方法甚至加剧了攻击，因此要确保图规约方法的计算效率提升不会危及 GN 系统的安全性。

关于图缩减的鲁棒性与 GNN 后门攻击

On the Robustness of Graph Reduction Against GNN Backdoor

Many real-world datasets can be naturally represented as graphs, spanning a
wide range of domains. However, the increasing complexity and size of graph
datasets present significant challenges for analysis and computation. In
response, graph reduction techniques have gained prominence for simplifying
large graphs while preserving essential properties. In this survey, we aim to
provide a comprehensive understanding of graph reduction methods, including
graph sparsification, graph coarsening, and graph condensation. Specifically,
we establish a unified definition for these methods and introduce a
hierarchical taxonomy to categorize the challenges they address. Our survey
then systematically reviews the technical details of these methods and
emphasizes their practical applications across diverse scenarios. Furthermore,
we outline critical research directions to ensure the continued effectiveness
of graph reduction techniques, as well as provide a comprehensive paper list at
this https URL We hope this survey
will bridge literature gaps and propel the advancement of this promising field.

图简化技术是一种应对大型图数据集复杂性和规模的有效方法，本调查研究对图稀疏化、图粗化和图凝聚等简化方法进行了全面概述，并阐述了它们在不同场景中的实际应用，为该领域的发展提供了技术综述和重要研究方向。