HiMod: Hierarchical Modeling with Graph Perturbation for Enhanced Inductive Knowledge Graph Reasoning

Inductive reasoning aims to infer missing knowledge for unseen entities and relations. Existing methods exhibit limited generalization capabilities due to their dependence on localized structural patterns and inadequate handling of graph imbalance. To address these challenges, we propose a novel Hierarchical Modeling with Graph Perturbation-Enhanced Network (HiMod), which effectively integrates hierarchical relation modeling with a dynamic perturbation mechanism to enhance the generalization ability of inductive reasoning models. HiMod leverages a hierarchical relation modeling mechanism that maps specific relations to higher-level general concepts within a global semantic framework. This allows for capturing semantic commonalities across relations, enabling robust reasoning for unseen queries. Simultaneously, a dynamic perturbation mechanism is introduced to adjust perturbation strength based on node importance and graph sparsity, facilitating deeper exploration of the latent semantic space and mitigating the effects of graph imbalance. Extensive experiments on three benchmark inductive knowledge graph reasoning datasets demonstrate that HiMod achieves the most significant MRR improvements among the four split versions, with 11.17% on WN18RR, 4.61% on FB15K-237, and 5.47% on NELL-995. Our code is available at https://github.com/HubuKG/HiMod.