Real-world graphs generally have only one kind of tendency in their
connections. These connections are either homophily-prone or heterophily-prone.
While graphs with homophily-prone edges tend to connect nodes with the same
class (i.e., intra-class nodes), heterophily-prone edges tend to build
relationships between nodes with different classes (i.e., inter-class nodes).
Existing GNNs only take the original graph during training. The problem with
this approach is that it forgets to take into consideration the ``missing-half"
structural information, that is, heterophily-prone topology for homophily-prone
graphs and homophily-prone topology for heterophily-prone graphs. In our paper,
we introduce Graph cOmplementAry Learning, namely GOAL, which consists of two
components: graph complementation and complemented graph convolution. The first
component finds the missing-half structural information for a given graph to
complement it. The complemented graph has two sets of graphs including both
homophily- and heterophily-prone topology. In the latter component, to handle
complemented graphs, we design a new graph convolution from the perspective of
optimisation. The experiment results show that GOAL consistently outperforms
all baselines in eight real-world datasets.

本文提出了 Graph cOmplementAry Learning（GOAL）方法，包括两个组件：图互补和互补图卷积，以解决现有 GNN 仅在训练时考虑原始图的问题，实现了对同质和异质性度量进行无差别的图卷积，该方法在八个真实数据集上表现出色。