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

2025-08-26

AI Summary

To address poor interactivity and weak interpretability in large-scale academic knowledge graphs (e.g., AI and biomedical talent graphs), this paper proposes a WebGL–LLM agent–coordinated visual analytics framework. Methodologically, it leverages WebGL for real-time rendering and dynamic filtering of tens of thousands of nodes, while integrating a lightweight LLM agent to support natural-language querying, semantics-driven team recommendation, and traceable reasoning. Its key contribution is the first deep integration of interpretable LLM inference into the graph visualization pipeline—balancing high performance with decision transparency. The system incorporates 28,000 experts and 1,179 biomedical datasets, and has been deployed on a public demonstration platform. In interdisciplinary collaborator discovery tasks, it significantly improves exploratory efficiency and recommendation credibility.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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/

Problem

• Interactive visualization for large knowledge graphs
• LLM integration for expert and dataset recommendations
• Overcoming limitations of traditional graph tools

Innovation

• Interactive WebGL visualization for large knowledge graphs
• LLM agents providing transparent AI-driven recommendations
• Integration of responsive exploration and filtering functionalities