  Graph Neural Networks (GNNs) are vital in data science but are increasingly
susceptible to adversarial attacks. To help researchers develop more robust GNN
models, it's essential to focus on designing strong attack models as
foundational benchmarks and guiding references. Among adversarial attacks,
gray-box poisoning attacks are noteworthy due to their effectiveness and fewer
constraints. These attacks exploit GNNs' need for retraining on updated data,
thereby impacting their performance by perturbing these datasets. However,
current research overlooks the real-world scenario of incomplete graphs.To
address this gap, we introduce the Robust Incomplete Deep Attack Framework
(RIDA). It is the first algorithm for robust gray-box poisoning attacks on
incomplete graphs. The approach innovatively aggregates distant vertex
information and ensures powerful data utilization.Extensive tests against 9
SOTA baselines on 3 real-world datasets demonstrate RIDA's superiority in
handling incompleteness and high attack performance on the incomplete graph.
