  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.
