论文标题
关系感知能量的下降步骤产生异质图神经网络
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks
论文作者
论文摘要
异质图神经网络（GNNS）在半监督学习设置中在节点分类任务上实现了强大的性能。但是，与更简单的GNN案例一样，基于消息的异质GNN可能难以在抵抗深度模型中可能发生的过度厚度之间取得平衡，并捕获图形结构化数据的长期依赖性。此外，由于不同类型的节点之间的异质关系不同，这种权衡的复杂性在异质图案例中复杂化。为了解决这些问题，我们提出了一种新型的异质GNN结构，其中层来自降低新型关系能量函数的优化步骤。相应的最小化器相对于能量函数参数是完全可区分的，因此可以应用双光线优化来有效地学习功能形式，其最小值为后续分类任务提供了最佳节点表示。特别是，这种方法使我们能够在不同的节点类型之间建模各种杂质关系，同时避免过度平滑效果。 8个异质图基准的实验结果表明，我们提出的方法可以达到竞争性节点分类精度
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
