[2603.20204] Measuring Research Convergence in Interdisciplinary Teams Using Large Language Models and Graph Analytics

Computer Science > Computers and Society arXiv:2603.20204 (cs) [Submitted on 26 Feb 2026] Title:Measuring Research Convergence in Interdisciplinary Teams Using Large Language Models and Graph Analytics Authors:Wenwen Li, Yuanyuan Tian, Sizhe Wang, Amber Wutich, Paul Westerhoff, Sarah Porter, Anais Roque, Jobayer Hossain, Patrick Thomson, Rhett Larson, Michael Hanemann View a PDF of the paper titled Measuring Research Convergence in Interdisciplinary Teams Using Large Language Models and Graph Analytics, by Wenwen Li and 10 other authors View PDF HTML (experimental) Abstract:Understanding how interdisciplinary research teams converge on shared knowledge is a persistent challenge. This paper presents a novel, multi-layer, AI-driven analytical framework for mapping research convergence in interdisciplinary teams. The framework integrates large language models (LLMs), graph-based visualization and analytics, and human-in-the-loop evaluation to examine how research viewpoints are shared, influenced, and integrated over time. LLMs are used to extract structured viewpoints aligned with the \emph{Needs-Approach-Benefits-Competition (NABC)} framework and to infer potential viewpoint flows across presenters, forming a common semantic foundation for three complementary analyses: (1) similarity-based qualitative analysis to identify two key types of viewpoints, popular and unique, for building convergence, (2) quantitative cross-domain influence analysis using network centrality measu...