Graph Neural Networks (GNNs) have emerged as powerful tools for learning over structured data, including text-attributed graphs (TAGs), which are common in domains such as citation networks, social platforms, and knowledge graphs. GNNs are not inherently interpretable and thus, many explanation methods have been proposed. However, existing explanation methods often struggle to generate interpretable, fine-grained rationales, especially when node attributes include rich natural language. In this work, we introduce GSPELL, a lightweight, post-hoc framework that uses large language models (LLMs) to generate faithful and interpretable explanations for GNN predictions. GSPELL projects GNN node embeddings into the LLM embedding space and constructs hybrid prompts that interleave soft prompts with textual inputs from the graph structure. This enables the LLM to reason about GNN internal representations and to produce natural-language explanations, along with concise explanation subgraphs. Our experiments across real-world TAG datasets demonstrate that GSPELL achieves a favorable trade-off between fidelity and sparsity, while improving human-centric metrics such as insightfulness. GSPELL sets a new direction for LLM-based explainability in graph learning by aligning GNN internals with human reasoning.

翻译：图神经网络（GNN）已成为处理结构化数据（包括文本属性图）的强大工具，这类图在引文网络、社交平台和知识图谱等领域中普遍存在。GNN本身不具备固有可解释性，因此已有多种解释方法被提出。然而，现有解释方法往往难以生成可解释的细粒度解释依据，尤其是当节点属性包含丰富的自然语言时。本文提出轻量级事后解释框架GSPELL，该框架利用大语言模型（LLM）为GNN预测生成可信且可解释的说明。GSPELL将GNN节点嵌入投影至LLM嵌入空间，并构建交错包含软提示与图结构文本输入的混合提示，使得LLM能够推理GNN内部表征并生成自然语言解释，同时辅以简洁的解释性子图。我们在真实文本属性图数据集上的实验表明，GSPELL在保真度与稀疏性之间实现了理想权衡，同时提升了洞察力等人文指标。GSPELL通过将GNN内部机制与人类推理相融合，为基于LLM的图学习可解释性开辟了新方向。