Refining Latent Homophilic Structures over Heterophilic Graphs for
  Robust Graph Convolution Networks
Chenyang Qiu Guoshun Nan Tianyu Xiong Wendi Deng Di Wang Zhiyang Teng Lijuan Sun Qimei Cui Xiaofeng Tao
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.