A Versatile Graph Learning Approach through LLM-based Agent
This addresses the need for more adaptable graph learning techniques in real-world applications, though it appears incremental as it builds on existing LLM and agent-based methods.
The paper tackles the problem of developing versatile graph learning methods for diverse graphs and tasks by proposing GL-Agent, an approach using LLM-based agents to customize procedures, achieving comparable performance and low resource cost in evaluations.
Designing versatile graph learning approaches is important, considering the diverse graphs and tasks existing in real-world applications. Existing methods have attempted to achieve this target through automated machine learning techniques, pre-training and fine-tuning strategies, and large language models. However, these methods are not versatile enough for graph learning, as they work on either limited types of graphs or a single task. In this paper, we propose to explore versatile graph learning approaches with LLM-based agents, and the key insight is customizing the graph learning procedures for diverse graphs and tasks. To achieve this, we develop several LLM-based agents, equipped with diverse profiles, tools, functions and human experience. They collaborate to configure each procedure with task and data-specific settings step by step towards versatile solutions, and the proposed method is dubbed GL-Agent. By evaluating on diverse tasks and graphs, the correct results of the agent and its comparable performance showcase the versatility of the proposed method, especially in complex scenarios.The low resource cost and the potential to use open-source LLMs highlight the efficiency of GL-Agent.