Uncovering novel scientific insights with a synergistic GNN-LLM framework

The exponential growth of scientific literature demands intelligent systems capable of uncovering emerging knowledge associations and fostering creativity. While graph neural networks (GNNs) excel at modeling literature structures, their static temporal modeling and lack of semantic awareness limit the discovery of interpretable signals. Conversely, large language models (LLMs) offer deep semantic reasoning but struggle to find non-obvious, structurally-grounded patterns without structured input. To address these limitations, this paper proposes a multi-stage GNN-LLM framework that integrates structural pattern recognition and semantic interpretation for scientific knowledge discovery. The framework begins with a Semantic-Enhanced Temporal Graph Network (SE-TGN), which embeds paper-level semantic information into an event-based temporal GNN to identify emerging keyword associations. These structurally grounded candidates are refined through the Contextual Re-ranking and Evaluation Framework (CREF), which leverages LLM capabilities to assess contextual novelty and relevance. Finally, the Generative Interpretation and Contextualization (GIC) produces human-readable explanations and research prompts to support innovation. Experiments in two scientific domains demonstrate the effectiveness of the framework in discovering semantically rich, contextually grounded, and forward-looking knowledge associations, illustrating its potential to support interpretable and creativity-driven scientific exploration.