Many real-world data can be modeled as heterogeneous graphs that contain multiple types of nodes and edges. Meanwhile, due to excellent performance, heterogeneous graph neural networks (GNNs) have received more and more attention. However, the existing work mainly focuses on the design of novel GNN models, while ignoring another important issue that also has a large impact on the model performance, namely the missing attributes of some node types. The handcrafted attribute completion requires huge expert experience and domain knowledge. Also, considering the differences in semantic characteristics between nodes, the attribute completion should be fine-grained, i.e., the attribute completion operation should be node-specific. Moreover, to improve the performance of the downstream graph learning task, attribute completion and the training of the heterogeneous GNN should be jointly optimized rather than viewed as two separate processes. To address the above challenges, we propose a differentiable attribute completion framework called AutoAC for automated completion operation search in heterogeneous GNNs. We first propose an expressive completion operation search space, including topology-dependent and topology-independent completion operations. Then, we propose a continuous relaxation schema and further propose a differentiable completion algorithm where the completion operation search is formulated as a bi-level joint optimization problem. To improve the search efficiency, we leverage two optimization techniques: discrete constraints and auxiliary unsupervised graph node clustering. Extensive experimental results on real-world datasets reveal that AutoAC outperforms the SOTA handcrafted heterogeneous GNNs and the existing attribute completion method

翻译：许多真实世界的数据可建模为包含多种节点和边类型的异构图。与此同时，由于性能卓越，异构图神经网络（GNN）日益受到关注。然而，现有工作主要聚焦于新型GNN模型的设计，而忽略了同样对模型性能产生重大影响的另一个重要问题——即某些节点类型的属性缺失。手工属性补全需要大量的专家经验和领域知识。此外，考虑到节点间语义特征的差异，属性补全应是细粒度的，即属性补全操作应针对特定节点。同时，为提升下游图学习任务的性能，属性补全与异构图GNN的训练应联合优化，而非视为两个独立过程。针对上述挑战，我们提出一种名为AutoAC的可微分属性补全框架，用于异构GNN中自动化补全操作搜索。首先，我们构建了一个表达性强的补全操作搜索空间，包含拓扑依赖型和拓扑无关型补全操作。接着，我们提出一种连续松弛方案，并进一步设计可微分补全算法，将补全操作搜索建模为双层联合优化问题。为提升搜索效率，我们引入两种优化技术：离散约束和辅助无监督图节点聚类。在真实数据集上的大量实验结果表明，AutoAC优于现有手工设计的异构GNN模型和已有属性补全方法。