Year: 2023
Type: preprint
Abstract: In recent years, there have been remarkable advancements in node classification achieved by Graph Neural Networks (GNNs). However, they necessitate abundant high-quality labels to ensure promising performance. In contrast, Large Language Models (LLMs) exhibit impressive zero-shot proficiency on text-attributed graphs. Yet, they face challenges in efficiently processing structural data and suffer from high inference costs. In light of these observations, this work introduces a label-free node classification on graphs with LLMs pipeline, LLM-GNN. It amalgamates the strengths of both GNNs and LLMs while mitigating their limitations. Specifically, LLMs are leveraged to annotate a small portion of nodes and then GNNs are trained on LLMs' annotations to make predictions for the remaining large portion of nodes. The implementation of LLM-GNN faces a unique challenge: how can we actively select nodes for LLMs to annotate and consequently enhance the GNN training? How can we leverage LLMs to obtain annotations of high quality, representativeness, and diversity, thereby enhancing GNN performance with less cost? To tackle this challenge, we develop an annotation quality heuristic and leverage the confidence scores derived from LLMs to advanced node selection. Comprehensive experimental results validate the effectiveness of LLM-GNN. In particular, LLM-GNN can achieve an accuracy of 74.9% on a vast-scale dataset \products with a cost less than 1 dollar.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G125128811, G1432373144, Collaborative Research: Advanced Quantitative and Computational Methods for STEM Education Research, CAREER: Real-World Networks: Modeling and Analysis of Signed Networks with Positive and Negative Links, III:Medium:Computation and Communication Efficient Distributed Learning, Collaborative Research: SaTC: CORE: Medium: Safeguarding Next-Generation Emergency Services (NG-9-1-1) over Cellular Networks: From Design to Practice, Intelligent, Adaptive Program with Just-in-time Feedback for Preservice Teachers, Collaborative Research: III: Medium: Graph Neural Networks for Heterophilous Data: Advancing the Theory, Models, and Applications, TRTech-PGR: Maize Hub: An Open-source Simulation Platform & Resources Integrating Extensive Field Trial Data, DNA Sequences, and Historical Weather Records, G7452299184, TRTech-PGR: Connecting sequences to functions within and between species through computational modeling and experimental studies
OpenAlex ID: W4387559293