  Graph-structured data are the commonly used and have wide application
scenarios in the real world. For these diverse applications, the vast variety
of learning tasks, graph domains, and complex graph learning procedures present
challenges for human experts when designing versatile graph learning
approaches. Facing these challenges, large language models (LLMs) offer a
potential solution due to the extensive knowledge and the human-like
intelligence. This paper proposes a novel conceptual prototype for designing
versatile graph learning methods with LLMs, with a particular focus on the
"where" and "how" perspectives. From the "where" perspective, we summarize four
key graph learning procedures, including task definition, graph data feature
engineering, model selection and optimization, deployment and serving. We then
explore the application scenarios of LLMs in these procedures across a wider
spectrum. In the "how" perspective, we align the abilities of LLMs with the
requirements of each procedure. Finally, we point out the promising directions
that could better leverage the strength of LLMs towards versatile graph
learning methods.
