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

TL;DR

This paper introduces GSPELL, a novel framework using large language models to generate interpretable, faithful explanations for graph neural network predictions, especially on text-attributed graphs.

Contribution

GSPELL is a lightweight, post-hoc method that aligns GNN internals with LLM reasoning to produce natural language explanations and concise subgraph rationales.

Findings

GSPELL achieves a good balance between fidelity and sparsity.

It improves human-centric metrics like insightfulness.

Demonstrates effectiveness on real-world text-attributed graph datasets.

Abstract

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.