@inproceedings{pan-etal-2025-graphnarrator,
    title = "{G}raph{N}arrator: Generating Textual Explanations for Graph Neural Networks",
    author = "Pan, Bo  and
      Xiong, Zhen  and
      Wu, Guanchen  and
      Zhang, Zheng  and
      Zhang, Yifei  and
      Hu, Yuntong  and
      Zhao, Liang",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.2/",
    pages = "23--42",
    ISBN = "979-8-89176-251-0",
    abstract = "Graph representation learning has garnered significant attention due to its broad applications in various domains, such as recommendation systems and social network analysis. Despite advancements in graph learning methods, challenges still remain in explainability when graphs are associated with semantic features. In this paper, we present GraphNarrator, the first method designed to generate natural language explanations for Graph Neural Networks. GraphNarrator employs a generative language model that maps input-output pairs to explanations reflecting the model{'}s decision-making process. To address the lack of ground truth explanations to train the model, we propose first generating pseudo-labels that capture the model{'}s decisions from saliency-based explanations, then using Expert Iteration to iteratively train the pseudo-label generator based on training objectives on explanation quality. The high-quality pseudo-labels are finally utilized to train an end-to-end explanation generator model. Extensive experiments are conducted to demonstrate the effectiveness of GraphNarrator in producing faithful, concise, and human-preferred natural language explanations."
}