  Graph Neural Networks (GNNs) have achieved promising results in tasks such as
node classification and graph classification. However, recent studies reveal
that GNNs are vulnerable to backdoor attacks, posing a significant threat to
their real-world adoption. Despite initial efforts to defend against specific
graph backdoor attacks, there is no work on defending against various types of
backdoor attacks where generated triggers have different properties. Hence, we
first empirically verify that prediction variance under edge dropping is a
crucial indicator for identifying poisoned nodes. With this observation, we
propose using random edge dropping to detect backdoors and theoretically show
that it can efficiently distinguish poisoned nodes from clean ones.
Furthermore, we introduce a novel robust training strategy to efficiently
counteract the impact of the triggers. Extensive experiments on real-world
datasets show that our framework can effectively identify poisoned nodes,
significantly degrade the attack success rate, and maintain clean accuracy when
defending against various types of graph backdoor attacks with different
properties.
