From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context

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 tradeoff between fidelity and sparsity, while improving human-centric metrics such as insightfulness. GSPELL sets a new direction for LLMbased explainability in graph learning by aligning GNN internals with human reasoning. Dataset Description & Relevance Why It Fits Our Work CORA Citation network of CS papers with bag-of-words features. Standard benchmark for node classification on text-rich graphs. WIKICS Wikipedia CS articles linked by hyperlinks, with GloVe embeddings. Large, semantic-rich graph ideal for explanation evaluation. LIAR Fake news detection graph combining statements, speakers, topics. Challenging, heterogeneous graph testing method adaptability. AMAZON Product co-purchase network with 47 categories. Real-world, diverse e-commerce graph to test scalability.