@inproceedings{yang-etal-2025-graphagent,
    title = "{G}raph{A}gent: Agentic Graph Language Assistant",
    author = "Yang, Yuhao  and
      Tang, Jiabin  and
      Xia, Lianghao  and
      Zou, Xingchen  and
      Liang, Yuxuan  and
      Huang, Chao",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1339/",
    pages = "26360--26379",
    ISBN = "979-8-89176-332-6",
    abstract = "Real-world data combines structured (e.g., graph connections) and unstructured (e.g., text, visuals) formats, capturing explicit relationships (e.g., social links) and implicit semantic interdependencies (e.g., knowledge graphs). We propose GraphAgent, an automated agent pipeline addressing both explicit and implicit graph-enhanced semantic dependencies for predictive (e.g., node classification) and generative (e.g., text generation) tasks. GraphAgent integrates three components: (i) a Graph Generator Agent creating knowledge graphs for semantic dependencies; (ii) a Task Planning Agent interpreting user queries and formulating tasks via self-planning; and (iii) a Task Execution Agent automating task execution with tool matching. These agents combine language and graph language models to reveal complex relational and semantic patterns. Extensive experiments on diverse datasets validate GraphAgent{'}s effectiveness in graph-related predictive and text generative tasks. GraphAgent is open-sourced at: https://anonymous.4open.science/r/GraphAgent-Submit-6F52/."
}