NetBone Meets LLMs: Toward Explainable Backbone Extraction
Résumé
Despite recent progress in standardizing backbone extraction through tools like NetBone, interpreting the results remains challenging. This is especially true for non-expert users unfamiliar with the method assumptions. In this work, we extend NetBone to support interpretation through Large Language Models (LLMs). To support this, we propose a metadata-driven pipeline that automatically captures each step of the backbone extraction process, including network descriptions, method parameters, filtering settings, and structural properties. This structured metadata is used to prompt an LLM to generate contextual and method-aware explanations. We believe this extension would improve interpretability and helps users understand how method choices affect the structure and meaning of their results.
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