  Knowledge Graph (KG) inductive reasoning, which aims to infer missing facts
from new KGs that are not seen during training, has been widely adopted in
various applications. One critical challenge of KG inductive reasoning is
handling low-resource scenarios with scarcity in both textual and structural
aspects. In this paper, we attempt to address this challenge with Large
Language Models (LLMs). Particularly, we utilize the state-of-the-art LLMs to
generate a graph-structural prompt to enhance the pre-trained Graph Neural
Networks (GNNs), which brings us new methodological insights into the KG
inductive reasoning methods, as well as high generalizability in practice. On
the methodological side, we introduce a novel pretraining and prompting
framework ProLINK, designed for low-resource inductive reasoning across
arbitrary KGs without requiring additional training. On the practical side, we
experimentally evaluate our approach on 36 low-resource KG datasets and find
that ProLINK outperforms previous methods in three-shot, one-shot, and
zero-shot reasoning tasks, exhibiting average performance improvements by 20%,
45%, and 147%, respectively. Furthermore, ProLINK demonstrates strong
robustness for various LLM promptings as well as full-shot scenarios.
