Reasoning on large-scale knowledge graphs has been long dominated by embedding methods. While path-based methods possess the inductive capacity that embeddings lack, their scalability is limited by the exponential number of paths. Here we present A*Net, a scalable path-based method for knowledge graph reasoning. Inspired by the A* algorithm for shortest path problems, our A*Net learns a priority function to select important nodes and edges at each iteration, to reduce time and memory footprint for both training and inference. The ratio of selected nodes and edges can be specified to trade off between performance and efficiency. Experiments on both transductive and inductive knowledge graph reasoning benchmarks show that A*Net achieves competitive performance with existing state-of-the-art path-based methods, while merely visiting 10% nodes and 10% edges at each iteration. On a million-scale dataset ogbl-wikikg2, A*Net not only achieves a new state-of-the-art result, but also converges faster than embedding methods. A*Net is the first path-based method for knowledge graph reasoning at such scale.

翻译：大规模知识图谱的推理长期由嵌入方法主导。尽管路径方法具备嵌入方法所缺乏的归纳能力，但其可扩展性受到路径数量指数级增长的制约。本文提出A*Net——一种面向知识图谱推理的可扩展路径方法。受最短路径问题中A*算法的启发，我们的A*Net学习一种优先级函数，在每次迭代中选择重要节点和边，从而降低训练和推理的时间与内存开销。可通过指定所选节点和边的比例来权衡性能与效率。在直推式和归纳式知识图谱推理基准上的实验表明，A*Net在与现有最先进路径方法竞争时取得了同等性能，且每次迭代仅访问10%的节点和10%的边。在百万级数据集ogbl-wikikg2上，A*Net不仅取得了新的最先进结果，其收敛速度还快于嵌入方法。A*Net是首个在此规模下实现知识图谱推理的路径方法。