Graph convolution networks (GCNs) are extensively utilized in various graph
tasks to mine knowledge from spatial data. Our study marks the pioneering
attempt to quantitatively investigate the GCN robustness over omnipresent
heterophilic graphs for node classification. We uncover that the predominant
vulnerability is caused by the structural out-of-distribution (OOD) issue. This
finding motivates us to present a novel method that aims to harden GCNs by
automatically learning Latent Homophilic Structures over heterophilic graphs.
We term such a methodology as LHS. To elaborate, our initial step involves
learning a latent structure by employing a novel self-expressive technique
based on multi-node interactions. Subsequently, the structure is refined using
a pairwisely constrained dual-view contrastive learning approach. We
iteratively perform the above procedure, enabling a GCN model to aggregate
information in a homophilic way on heterophilic graphs. Armed with such an
adaptable structure, we can properly mitigate the structural OOD threats over
heterophilic graphs. Experiments on various benchmarks show the effectiveness
of the proposed LHS approach for robust GCNs.

我们研究了图卷积网络在节点分类中对普遍存在的异质图的鲁棒性，并发现主要的脆弱性源于结构上的分布不一致问题。为了解决这个问题，我们提出了一种新的方法，通过自动学习异质图上的潜在同质结构来加固 GCNs。这个方法被称为 LHS，通过多节点交互的新颖自表达技术学习潜在结构，然后使用双视图对比学习方法来细化结构。在各种基准测试上的实验表明，该方法对于鲁棒的 GCNs 具有有效性。