From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context
This work addresses the need for better interpretability in GNNs for domains like citation networks and social platforms, representing an incremental advance by combining LLMs with existing explanation methods.
The authors tackled the problem of generating interpretable explanations for Graph Neural Network (GNN) predictions on text-attributed graphs by introducing LOGIC, a framework that uses large language models (LLMs) to produce natural language explanations and subgraphs, achieving improved human-centric metrics like insightfulness.
Graph Neural Networks (GNNs) have emerged as powerful tools for learning over structured data, including text-attributed graphs, 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 LOGIC, a lightweight, post-hoc framework that uses large language models (LLMs) to generate faithful and interpretable explanations for GNN predictions. LOGIC 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 produce natural language explanations along with concise explanation subgraphs. Our experiments across four real-world TAG datasets demonstrate that LOGIC achieves a favorable trade-off between fidelity and sparsity, while significantly improving human-centric metrics such as insightfulness. LOGIC sets a new direction for LLM-based explainability in graph learning by aligning GNN internals with human reasoning.