Graph neural networks (GNNs) learn node representations by passing and
aggregating messages between neighboring nodes. GNNs have been applied
successfully in several application domains and achieved promising performance.
However, GNNs could be vulnerable to structural noise because of the message
passing mechanism where noise may be propagated through the entire graph.
Although a series of robust GNNs have been proposed, they are evaluated with
different structural noises, and it lacks a systematic comparison with
consistent settings. In this work, we conduct a comprehensive and systematical
comparative study on different types of robust GNNs under consistent structural
noise settings. From the noise aspect, we design three different levels of
structural noises, i.e., local, community, and global noises. From the model
aspect, we select some representative models from sample-based, revision-based,
and construction-based robust GNNs. Based on the empirical results, we provide
some practical suggestions for robust GNNs selection.

该研究对不同类型的鲁棒图神经网络在结构噪声表现下进行了全面和系统的比较，并提供了鲁棒性图神经网络选择的实用建议。

结构噪声下稳健图神经网络的比较研究

A Comparative Study on Robust Graph Neural Networks to Structural Noises

Graph neural networks (GNNs) are an emerging model for learning graph
embeddings and making predictions on graph structured data. However, robustness
of graph neural networks is not yet well-understood. In this work, we focus on
node structural identity predictions, where a representative GNN model is able
to achieve near-perfect accuracy. We also show that the same GNN model is not
robust to addition of structural noise, through a controlled dataset and set of
experiments. Finally, we show that under the right conditions, graph-augmented
training is capable of significantly improving robustness to structural noise.

该研究聚焦于图神经网络的鲁棒性问题，尤其是节点结构预测中添加结构噪声对模型稳健性的影响，研究表明，通过图增强训练，可以显著提高图神经网络的鲁棒性。