Graph2Idea:Retrieval-Augmented Scientific Idea Generation with Graph-Structured Contexts

Graph2Idea is a novel framework for retrieval-augmented scientific idea generation that utilizes knowledge graphs to enhance the context provided to Large Language Models (LLMs). By transforming retrieved literature into structured knowledge triples and constructing a target-centered knowledge graph, Graph2Idea improves the relevance and clarity of input data, resulting in significant performance gains on a scientific idea generation benchmark—improving Novelty from 0.45 to 0.52, Quality from 0.24 to 0.29, and Feasibility from 0.22 to 0.28 compared to the strongest baseline. This approach emphasizes the importance of structured relational evidence in generating high-quality research ideas, making it a valuable tool for practitioners working in scientific discovery and LLM applications.