Interactive Graph Visualization and TeamingRecommendation in an Interdisciplinary Project'sTalent Knowledge Graph

TL;DR

This paper presents an interactive visualization system for a large biomedical and AI talent knowledge graph, integrating WebGL and LLM reasoning to improve exploration, filtering, and AI-driven recommendations for research collaboration.

Contribution

It introduces a novel framework combining WebGL visualization with LLM agents for large-scale knowledge graph exploration and recommendation, addressing limitations of traditional tools.

Findings

Enhanced interactive exploration of large knowledge graphs

Effective AI-driven recommendations with justifications

Potential to improve collaboration and dataset discovery

Abstract

Interactive visualization of large scholarly knowledge graphs combined with LLM reasoning shows promise butremains under-explored. We address this gap by developing an interactive visualization system for the Cell Map forAI Talent Knowledge Graph (28,000 experts and 1,179 biomedical datasets). Our approach integrates WebGLvisualization with LLM agents to overcome limitations of traditional tools such as Gephi, particularly for large-scaleinteractive node handling. Key functionalities include responsive exploration, filtering, and AI-drivenrecommendations with justifications. This integration can potentially enable users to effectively identify potentialcollaborators and relevant dataset users within biomedical and AI research communities. The system contributes anovel 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 knowledgegraphs, improving information representation and decision-making. Demo: https://cm4aikg.vercel.app/