AutoAC：面向异构图神经网络的自动属性补全
AutoAC: Towards Automated Attribute Completion for Heterogeneous Graph Neural Network
摘要：
许多真实世界的数据可以建模为包含多种类型的节点和边的异构图。与此同时，异构图神经网络（GNNs）由于其优异的性能受到了越来越多的关注。然而，现有的工作主要集中在设计新颖的GNN模型，而忽略了另一个重要的问题，也有很大的影响模型的性能，即一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，考虑到节点之间语义特征的差异，属性补全应该是细粒度的，即，属性完成操作应该是节点特定的。此外，为了提高下游图学习任务的性能，属性完成和异构GNN的训练应该联合优化，而不是被视为两个单独的过程。为了解决上述挑战，我们提出了一个称为AutoAC的可区分属性补全框架，用于在异构GNN中进行自动补全操作搜索。我们首先提出了一个表达的完成操作搜索空间，包括拓扑相关和拓扑无关的完成操作。然后，我们提出了一个连续松弛模式，并进一步提出了一个可微的完成算法，其中的完成操作搜索制定为一个双层联合优化问题。为了提高搜索效率，我们利用两种优化技术：离散约束和辅助无监督图节点聚类。在真实世界数据集上的大量实验结果表明，AutoAC优于SOTA手工制作的异构GNN和现有的属性完成方法
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
