Graph neural networks (GNNs) have shown significant performance in various practical applications due to their strong learning capabilities. Backdoor attacks are a type of attack that can produce hidden on machine models. GNNs take backdoor datasets as input an adversary-specified output poisoned data but perform normally clean data, which grave implications for applications. under-researched t...

Adversarial attacks on Graph Neural Networks (GNNs) reveal their security vulnerabilities, limiting adoption in safety-critical applications. However, existing attack strategies rely the knowledge of either GNN model being used or predictive task attacked. Is this necessary? For example, a graph may be for multiple downstream tasks unknown to practical attacker. It is thus important test vulner...

Nowadays, Knowledge graphs (KGs) have been playing a pivotal role in AI-related applications. Despite the large sizes, existing KGs are far from complete and comprehensive. In order to continuously enrich KGs, automatic knowledge construction update mechanisms usually utilized, which inevitably bring plenty of noise. However, most graph embedding (KGE) methods assume that all triple facts corre...