Inductive logic reasoning is a fundamental task in graph analysis, which aims to generalize patterns from data. This task has been extensively studied for traditional graph representations, such as knowledge graphs (KGs), using techniques like inductive logic programming (ILP). Existing ILP methods assume learning from KGs with static facts and binary relations. Beyond KGs, graph structures are widely present in other applications such as procedural instructions, scene graphs, and program executions. While ILP is beneficial for these applications, applying it to those graphs is nontrivial: they are more complex than KGs, which usually involve timestamps and n-ary relations, effectively a type of hypergraph with temporal events. In this work, we propose temporal inductive logic reasoning (TILR), an ILP method that reasons on temporal hypergraphs. To enable hypergraph reasoning, we introduce the multi-start random B-walk, a novel graph traversal method for hypergraphs. By combining it with a path-consistency algorithm, TILR learns logic rules by generalizing from both temporal and relational data. To address the lack of hypergraph benchmarks, we create and release two temporal hypergraph datasets: YouCook2-HG and nuScenes-HG. Experiments on these benchmarks demonstrate that TILR achieves superior reasoning capability over various strong baselines.

翻译：归纳逻辑推理是图分析中的基础任务，旨在从数据中泛化规律。该任务在传统图表示（如知识图谱）中已得到广泛研究，常用技术包括归纳逻辑编程（ILP）。现有ILP方法假设从包含静态事实与二元关系的知识图谱中学习。除知识图谱外，图结构还广泛存在于过程指令、场景图、程序执行等其他应用场景中。尽管ILP对这些应用具有价值，但将其应用于此类图结构并非易事：这些图通常涉及时间戳与多元关系，本质上属于带时序事件的超图，其复杂度远超知识图谱。本文提出时序归纳逻辑推理（TILR），一种针对时序超图进行推理的ILP方法。为实现超图推理，我们引入多起点随机B路径——一种新颖的超图遍历方法。通过将其与路径一致性算法相结合，TILR能够从时序数据和关系数据中泛化学习逻辑规则。针对超图基准数据缺失问题，我们创建并发布了两个时序超图数据集：YouCook2-HG和nuScenes-HG。在这些基准上的实验表明，TILR相较于多种强基线模型展现出更优越的推理能力。