When Do LLMs Help With Node Classification? A Comprehensive Analysis

TL;DR

This paper systematically compares LLM-based methods for node classification, providing guidelines and a comprehensive testbed to evaluate their performance across various datasets and settings.

Contribution

It introduces LLMNodeBed, a codebase and testbed for node classification with LLMs, and offers extensive experimental insights to guide future research.

Findings

LLM methods outperform traditional ones in semi-supervised settings

Graph Foundation Models outperform open-source LLMs but not GPT-4o in zero-shot

Performance depends on learning paradigms, homophily, and model size

Abstract

Node classification is a fundamental task in graph analysis, with broad applications across various fields. Recent breakthroughs in Large Language Models (LLMs) have enabled LLM-based approaches for this task. Although many studies demonstrate the impressive performance of LLM-based methods, the lack of clear design guidelines may hinder their practical application. In this work, we aim to establish such guidelines through a fair and systematic comparison of these algorithms. As a first step, we developed LLMNodeBed, a comprehensive codebase and testbed for node classification using LLMs. It includes 10 homophilic datasets, 4 heterophilic datasets, 8 LLM-based algorithms, 8 classic baselines, and 3 learning paradigms. Subsequently, we conducted extensive experiments, training and evaluating over 2,700 models, to determine the key settings (e.g., learning paradigms and homophily) and components (e.g., model size and prompt) that affect performance. Our findings uncover 8 insights, e.g., (1) LLM-based methods can significantly outperform traditional methods in a semi-supervised setting, while the advantage is marginal in a supervised setting; (2) Graph Foundation Models can beat open-source LLMs but still fall short of strong LLMs like GPT-4o in a zero-shot setting. We hope that the release of LLMNodeBed, along with our insights, will facilitate reproducible research and inspire future studies in this field. Codes and datasets are released at \href{https://llmnodebed.github.io/}{\texttt{https://llmnodebed.github.io/}}.