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.

翻译：现实世界的图通常只有一种连接倾向，即同质偏好或异质偏好。同质偏好边倾向于连接同类节点（即类内节点），而异质偏好边则倾向于连接不同类节点（即类间节点）。现有图神经网络仅利用原始图进行训练，这种做法忽视了“缺失的一半”结构信息，即同质偏好图中缺失的异质偏好拓扑，以及异质偏好图中缺失的同质偏好拓扑。本文提出图互补学习（GOAL），包含两个组件：图互补与互补图卷积。第一个组件为给定图寻找缺失的一半结构信息以进行补全，补全后的图包含同质偏好与异质偏好两组拓扑结构；第二个组件则从优化角度设计了一种新的图卷积来处理补全后的图。实验结果表明，GOAL在八个真实世界数据集上均优于所有基线方法。