# Measuring Research Convergence in Interdisciplinary Teams Using Large Language Models and Graph Analytics

arXiv: https://arxiv.org/abs/2603.20204 · HTML: https://mlsanity.com/paper/2603.20204/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 52 references that are in the map.
- First posted on arXiv 26 Feb 2026.

## Builds on (most cited first, 5 of 5)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [A comparison of two approaches for measuring interdisciplinary research output: the disciplinary diversity of authors vs the disciplinary diversity of the reference list](https://mlsanity.com/paper/2103.14856/) (2021)
- [PageRank Algorithm using Eigenvector Centrality -- New Approach](https://mlsanity.com/paper/2201.05469/) (2022)
- [Grand challenges and emergent modes of convergence science](https://mlsanity.com/paper/2103.11547/) (2021)
- [GeoGraphViz: Geographically Constrained 3D Force-Directed Graph for Knowledge Graph Visualization](https://mlsanity.com/paper/2304.09864/) (2023)

## Abstract (arXiv)

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 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 measures, and (3) temporal viewpoint flow analysis to capture convergence dynamics. To address uncertainty in LLM-based inference, the framework incorporates expert validation through structured surveys and cross-layer consistency checks. A case study on water insecurity in underserved communities as part of the Arizona Water Innovation Initiatives demonstrates increasing viewpoint convergence and domain-specific influence patterns, illustrating the value of the proposed AI-enabled approach for research convergence analysis.