Meta-structures are widely used to define which subset of neighbors to aggregate information in heterogeneous information networks (HINs). In this work, we investigate existing meta-structures, including meta-path and meta-graph, and observe that they are initially designed manually with fixed patterns and hence are insufficient to encode various rich semantic information on diverse HINs. Through reflection on their limitation, we define a new concept called meta-multigraph as a more expressive and flexible generalization of meta-graph, and propose a stable differentiable search method to automatically optimize the meta-multigraph for specific HINs and tasks. As the flexibility of meta-multigraphs may propagate redundant messages, we further introduce a complex-to-concise (C2C) meta-multigraph that propagates messages from complex to concise along the depth of meta-multigraph. Moreover, we observe that the differentiable search typically suffers from unstable search and a significant gap between the meta-structures in search and evaluation. To this end, we propose a progressive search algorithm by implicitly narrowing the search space to improve search stability and reduce inconsistency. Extensive experiments are conducted on six medium-scale benchmark datasets and one large-scale benchmark dataset over two representative tasks, i.e., node classification and recommendation. Empirical results demonstrate that our search methods can automatically find expressive meta-multigraphs and C2C meta-multigraphs, enabling our model to outperform state-of-the-art heterogeneous graph neural networks.

翻译：元结构被广泛应用于定义异质信息网络(HINs)中需聚合信息的邻居子集。本文通过分析现有的元结构（包括元路径和元图），发现它们最初采用固定模式进行手工设计，因此难以充分编码不同HINs中丰富的语义信息。基于对其局限性的反思，我们定义了一种称为元多图的新概念，作为元图更具表达力和灵活性的泛化形式，并提出一种稳定的可微搜索方法来自动优化特定HINs和任务的元多图。由于元多图的灵活性可能传播冗余信息，我们进一步引入了从复杂到简洁(C2C)的元多图，该结构沿元多图深度方向将信息从复杂传递至简洁。此外，我们观察到可微搜索通常存在搜索不稳定以及搜索与评估阶段元结构之间的显著差异。为此，我们提出一种渐进式搜索算法，通过隐式缩小搜索空间来提升搜索稳定性并降低不一致性。我们在六个中等规模基准数据集和一个大规模基准数据集上，针对节点分类和推荐这两个代表性任务进行了大量实验。实验结果表明，我们的搜索方法能够自动发现具有表达力的元多图与C2C元多图，使模型性能优于最先进的异质图神经网络。