Heterogeneous information networks (HINs) are widely employed for describing
real-world data with intricate entities and relationships. To automatically
utilize their semantic information, graph neural architecture search has
recently been developed on various tasks of HINs. Existing works, on the other
hand, show weaknesses in instability and inflexibility. To address these
issues, we propose a novel method called Partial Message Meta Multigraph search
(PMMM) to automatically optimize the neural architecture design on HINs.
Specifically, to learn how graph neural networks (GNNs) propagate messages
along various types of edges, PMMM adopts an efficient differentiable framework
to search for a meaningful meta multigraph, which can capture more flexible and
complex semantic relations than a meta graph. The differentiable search
typically suffers from performance instability, so we further propose a stable
algorithm called partial message search to ensure that the searched meta
multigraph consistently surpasses the manually designed meta-structures, i.e.,
meta-paths. Extensive experiments on six benchmark datasets over two
representative tasks, including node classification and recommendation,
demonstrate the effectiveness of the proposed method. Our approach outperforms
the state-of-the-art heterogeneous GNNs, finds out meaningful meta multigraphs,
and is significantly more stable.

本文提出了一种名为部分信息元多图搜索（PMMM）的新方法，用于自动优化 HIN 上的神经结构设计，其采用可微分方法搜索有意义的元多图，以捕获比元图更灵活和复杂的语义关系，并证明其优于现有作品和是稳定的算法。