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Heterogeneous information networks (HINs) are widely employed for describingreal-world data with intricate entities and relationships. To automaticallyutilize their semantic information, graph neural architecture search hasrecently been developed on various tasks of HINs. Existing works, on the otherhand, show weaknesses in instability and inflexibility. To address theseissues, 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 messagesalong various types of edges, PMMM adopts an efficient differentiable frameworkto search for a meaningful meta multigraph, which can capture more flexible andcomplex semantic relations than a meta graph. The differentiable searchtypically suffers from performance instability, so we further propose a stablealgorithm called partial message search to ensure that the searched metamultigraph consistently surpasses the manually designed meta-structures, i.e.,meta-paths. Extensive experiments on six benchmark datasets over tworepresentative tasks, including node classification and recommendation,demonstrate the effectiveness of the proposed method. Our approach outperformsthe state-of-the-art heterogeneous GNNs, finds out meaningful meta multigraphs,and is significantly more stable.

Differentiable Meta Multigraph Search with Partial Message Propagation on Heterogeneous Information Networks