Heterogeneous graph neural networks (GNNs) achieve strong performance on node classification tasks in a semi-supervised learning setting. However, as in the simpler homogeneous GNN case, message-passing-based heterogeneous GNNs may struggle to balance between resisting the oversmoothing that may occur in deep models, and capturing long-range dependencies of graph structured data. Moreover, the complexity of this trade-off is compounded in the heterogeneous graph case due to the disparate heterophily relationships between nodes of different types. To address these issues, we propose a novel heterogeneous GNN architecture in which layers are derived from optimization steps that descend a novel relation-aware energy function. The corresponding minimizer is fully differentiable with respect to the energy function parameters, such that bilevel optimization can be applied to effectively learn a functional form whose minimum provides optimal node representations for subsequent classification tasks. In particular, this methodology allows us to model diverse heterophily relationships between different node types while avoiding oversmoothing effects. Experimental results on 8 heterogeneous graph benchmarks demonstrates that our proposed method can achieve competitive node classification accuracy

翻译：在半监督的学习环境中,异质图形神经网络(GNNs)在节点分类任务上取得了很强的成绩。然而,正如在简单单一的GNN(GNN)案例中一样,基于信息传递的多元性GNN(GNN)可能会在抵制深层模型中可能出现的过度悬浮现象和捕捉图形结构数据的长期依赖性之间难以取得平衡。此外,由于不同类型节点之间不同的杂交关系,这种权衡的复杂性在异质图形中更为复杂。为了解决这些问题,我们提议了一个新型的异性GNN(GNN)结构,在这种结构中,从优化步骤中产生层值,并降下一个新的对关系有认识的能源功能功能。相应的最小化在能源功能参数方面是完全不同的,因此可以应用双级优化来有效学习一种功能形式,这种形式最起码能为今后的分类任务提供最佳节点表示。特别是,这种方法使我们能够模拟不同节点类型之间不同的不同结构间不同结构的关系,同时避免过度测量效应。关于8个差异图形基准的实验结果表明,我们所提议的方法可以实现竞争性节点分类的准确性。