Graph neural networks (GNNs) have achieved remarkable success in various
tasks, however, their vulnerability to adversarial attacks raises concerns for
the real-world applications. Existing defense methods can resist some attacks,
but suffer unbearable performance degradation under other unknown attacks. This
is due to their reliance on either limited observed adversarial examples to
optimize (adversarial training) or specific heuristics to alter graph or model
structures (graph purification or robust aggregation). In this paper, we
propose an Invariant causal DEfense method against adversarial Attacks (IDEA),
providing a new perspective to address this issue. The method aims to learn
causal features that possess strong predictability for labels and invariant
predictability across attacks, to achieve graph adversarial robustness. Through
modeling and analyzing the causal relationships in graph adversarial attacks,
we design two invariance objectives to learn the causal features. Extensive
experiments demonstrate that our IDEA significantly outperforms all the
baselines under both poisoning and evasion attacks on five benchmark datasets,
highlighting the strong and invariant predictability of IDEA. The
implementation of IDEA is available at
this https URL

提出了一种旨在学习具有强预测性和不变性的因果特征以实现图形对抗鲁棒性的不变因果防御方法，称为 Invariant causal DEfense method against adversarial Attacks (IDEA)，并通过实验证明，在五个基准数据集上 IDEA 在毒害和逃避攻击下显着优于所有基线。