Graph Neural Networks (GNNs) have shown to be powerful tools for graph
analytics. The key idea is to recursively propagate and aggregate information
along edges of the given graph. Despite their success, however, the existing
GNNs are usually sensitive to the quality of the input graph. Real-world graphs
are often noisy and contain task-irrelevant edges, which may lead to suboptimal
generalization performance in the learned GNN models. In this paper, we propose
PTDNet, a parameterized topological denoising network, to improve the
robustness and generalization performance of GNNs by learning to drop
task-irrelevant edges. PTDNet prunes task-irrelevant edges by penalizing the
number of edges in the sparsified graph with parameterized networks. To take
into consideration of the topology of the entire graph, the nuclear norm
regularization is applied to impose the low-rank constraint on the resulting
sparsified graph for better generalization. PTDNet can be used as a key
component in GNN models to improve their performances on various tasks, such as
node classification and link prediction. Experimental studies on both synthetic
and benchmark datasets show that PTDNet can improve the performance of GNNs
significantly and the performance gain becomes larger for more noisy datasets.

本文提出了一种基于参数化拓扑去噪网络的 GNN 模型（PTDNet），可以通过学习去除任务无关的边来提高 GNN 模型的稳健性和泛化性能，并在各种任务中（如节点分类和链接预测）均有显著的性能提升，特别是在噪声数据集上性能提升更为明显。