Interactive Graph Visualization and Teaming Recommendation in an Interdisciplinary Project's Talent Knowledge Graph

Interactive visualization of large scholarly knowledge graphs combined with LLM reasoning shows promise but remains under‐explored. We address this gap by developing an interactive visualization system for the Cell Map for AI Talent Knowledge Graph (28,000 experts and 1,179 biomedical datasets). Our approach integrates WebGL visualization with LLM agents to overcome limitations of traditional tools such as Gephi, particularly for large‐scale interactive node handling. Key functionalities include responsive exploration, filtering, and AI‐driven recommendations with justifications. This integration can potentially enable users to effectively identify potential collaborators and relevant dataset users within biomedical and AI research communities. The system contributes a novel framework that enhances knowledge graph exploration through intuitive visualization and transparent, LLM‐guided recommendations. This adaptable solution extends beyond the CM4AI community to other large knowledge graphs, improving information representation and decision‐making. Demo: https://cm4aikg.vercel.app/