  Large Language Models (LLMs) have demonstrated remarkable performance across
various natural language processing tasks. Recently, several LLMs-based
pipelines have been developed to enhance learning on graphs with text
attributes, showcasing promising performance. However, graphs are well-known to
be susceptible to adversarial attacks and it remains unclear whether LLMs
exhibit robustness in learning on graphs. To address this gap, our work aims to
explore the potential of LLMs in the context of adversarial attacks on graphs.
Specifically, we investigate the robustness against graph structural and
textual perturbations in terms of two dimensions: LLMs-as-Enhancers and
LLMs-as-Predictors. Through extensive experiments, we find that, compared to
shallow models, both LLMs-as-Enhancers and LLMs-as-Predictors offer superior
robustness against structural and textual attacks.Based on these findings, we
carried out additional analyses to investigate the underlying causes.
Furthermore, we have made our benchmark library openly available to facilitate
quick and fair evaluations, and to encourage ongoing innovative research in
this field.
