Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models

TL;DR

This paper explores how large language models can be used to create versatile graph learning methods by analyzing key procedures and aligning LLM capabilities with these tasks.

Contribution

It introduces a conceptual framework for leveraging LLMs in various graph learning procedures, focusing on 'where' and 'how' perspectives.

Findings

Identifies four key graph learning procedures.

Explores LLM applications across these procedures.

Suggests future directions for LLM-enhanced graph learning.

Abstract

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.