  Graph neural network (GNN) with a powerful representation capability has been
widely applied to various areas, such as biological gene prediction, social
recommendation, etc. Recent works have exposed that GNN is vulnerable to the
backdoor attack, i.e., models trained with maliciously crafted training samples
are easily fooled by patched samples. Most of the proposed studies launch the
backdoor attack using a trigger that either is the randomly generated subgraph
(e.g., erd\H{o}s-r\'enyi backdoor) for less computational burden, or the
gradient-based generative subgraph (e.g., graph trojaning attack) to enable a
more effective attack. However, the interpretation of how is the trigger
structure and the effect of the backdoor attack related has been overlooked in
the current literature. Motifs, recurrent and statistically significant
sub-graphs in graphs, contain rich structure information. In this paper, we are
rethinking the trigger from the perspective of motifs, and propose a
motif-based backdoor attack, denoted as Motif-Backdoor. It contributes from
three aspects. (i) Interpretation: it provides an in-depth explanation for
backdoor effectiveness by the validity of the trigger structure from motifs,
leading to some novel insights, e.g., using subgraphs that appear less
frequently in the graph as the trigger can achieve better attack performance.
(ii) Effectiveness: Motif-Backdoor reaches the state-of-the-art (SOTA) attack
performance in both black-box and defensive scenarios. (iii) Efficiency: based
on the graph motif distribution, Motif-Backdoor can quickly obtain an effective
trigger structure without target model feedback or subgraph model generation.
Extensive experimental results show that Motif-Backdoor realizes the SOTA
performance on three popular models and four public datasets compared with five
baselines.
