Knowledge graphs (KGs) are widely acknowledged as incomplete, and new entities are constantly emerging in the real world. Inductive KG reasoning aims to predict missing facts for these new entities. Among existing models, graph neural networks (GNNs) based ones have shown promising performance for this task. However, they are still challenged by inefficient message propagation due to the distance and scalability issues. In this paper, we propose a new inductive KG reasoning model, MStar, by leveraging conditional message passing neural networks (C-MPNNs). Our key insight is to select multiple query-specific starting entities to expand the scope of progressive propagation. To propagate query-related messages to a farther area within limited steps, we subsequently design a highway layer to propagate information toward these selected starting entities. Moreover, we introduce a training strategy called LinkVerify to mitigate the impact of noisy training samples. Experimental results validate that MStar achieves superior performance compared with state-of-the-art models, especially for distant entities.

翻译：知识图谱（KGs）被广泛认为是不完备的，现实世界中不断涌现新实体。归纳知识图谱推理旨在为这些新实体预测缺失事实。在现有模型中，基于图神经网络（GNNs）的方法在此任务上已展现出良好性能。然而，由于距离和可扩展性问题导致的低效消息传播仍对其构成挑战。本文提出一种新的归纳知识图谱推理模型MStar，其利用条件消息传递神经网络（C-MPNNs）。我们的核心思路是选择多个查询相关的起始实体以拓展渐进传播的范围。为了在有限步数内将查询相关信息传播至更远区域，我们进一步设计高速公路层以向这些选定的起始实体传递信息。此外，我们引入一种名为LinkVerify的训练策略以减轻噪声训练样本的影响。实验结果验证了MStar相比现有先进模型具有更优性能，尤其对于远距离实体。