Crowdsourced Homophily Ties Based Graph Annotation Via Large Language Model
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917953680703488 |
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| author | Bu, Yu Zhu, Yulin Zhou, Kai |
| author_facet | Bu, Yu Zhu, Yulin Zhou, Kai |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09281 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Crowdsourced Homophily Ties Based Graph Annotation Via Large Language Model Bu, Yu Zhu, Yulin Zhou, Kai Social and Information Networks 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. |
| title | Crowdsourced Homophily Ties Based Graph Annotation Via Large Language Model |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2503.09281 |