Graph Neural Network (GNN) research has produced strategies to modify a graph's edges using gradients from a trained GNN, with the goal of network design. However, the factors which govern gradient-based editing are understudied, obscuring why edges are chosen and if edits are grounded in an edge's importance. Thus, we begin by analyzing the gradient computation in previous works, elucidating the factors that influence edits and highlighting the potential over-reliance on structural properties. Specifically, we find that edges can achieve high gradients due to structural biases, rather than importance, leading to erroneous edits when the factors are unrelated to the design task. To improve editing, we propose ORE, an iterative editing method that (a) edits the highest scoring edges and (b) re-embeds the edited graph to refresh gradients, leading to less biased edge choices. We empirically study ORE through a set of proposed design tasks, each with an external validation method, demonstrating that ORE improves upon previous methods by up to 50%.

图神经网络（GNN）研究提出了使用训练后的GNN梯度来修改图的边缘以实现网络设计的策略，然而，调节基于梯度的编辑的因素尚未得到充分研究，模糊了为何选择边缘以及编辑是否基于边缘的重要性。因此，我们首先分析了先前工作中的梯度计算，阐明了影响编辑的因素并突出了对结构属性的过度依赖。具体来说，我们发现边缘之所以能够获得高梯度是由于结构偏倚而非重要性，这会导致与设计任务无关的错误编辑。为了改善编辑过程，我们提出了ORE，一种迭代编辑方法，通过（a）编辑得分最高的边缘和（b）重新嵌入编辑后的图来刷新梯度，从而减少了边缘选择的偏见。我们通过一系列提出的设计任务进行了与外部验证方法相结合的ORE的实证研究，表明ORE的性能比先前方法提高了50%。

通过图神经网络进行网络设计：识别挑战和提高性能