Accurate graph annotation typically requires substantial labeled data, which is often challenging and resource-intensive to obtain. In this paper, we present Crowdsourced Homophily Ties Based Graph Annotation via Large Language Model (CSA-LLM), a novel approach that combines the strengths of crowdsourced annotations with the capabilities of large language models (LLMs) to enhance the graph annotation process. CSA-LLM harnesses the structural context of graph data by integrating information from 1-hop and 2-hop neighbors. By emphasizing homophily ties - key connections that signify similarity within the graph - CSA-LLM significantly improves the accuracy of annotations. Experimental results demonstrate that this method enhances the performance of Graph Neural Networks (GNNs) by delivering more precise and reliable annotations.
@article{arxiv.2503.09281,
title = {Crowdsourced Homophily Ties Based Graph Annotation Via Large Language Model},
author = {Yu Bu and Yulin Zhu and Kai Zhou},
journal= {arXiv preprint arXiv:2503.09281},
year = {2025}
}