LLM-Enabled Scientific Knowledge Diffusion Analysis

Abstract

Bibliometric and science-of-science studies have yielded valuable insights into co-authorship and citation networks, yet most analyses rely on static datasets and limited relation types. We introduce a multi-agent AI architecture that orchestrates specialized large language model (LLM) agents (ingestion, extraction, disambiguation, integration, and analysis) to build and query a comprehensive knowledge graph. Ingestion agents unify data from diverse sources such as OpenAlex, ORCID, ROR, USPTO, and custom web scrapers. Extraction agents harness LLMs to parse unstructured text. Disambiguation agents combine rule-based heuristics with LLM reasoning to resolve ambiguous authors and institutions. Integration agents assemble and cache a provenance-rich graph. An analysis agent translates natural language questions into graph queries and interprets results. This end-to-end pipeline produces a rich graph schema spanning authors, institutions, publications, patents, grants, topics, and temporal relations. Researcher mobility and knowledge diffusion are then modeled as timed automata, where each researcher node’s institutional transitions and accumulated attributes (such as publications, collaborators, and topic expertise) enable dynamic temporal reasoning. Results show that our multi-agent, graph-based system consistently outperforms standalone LLMs and research agents on complex temporal queries, entity disambiguation accuracy, and cross-entity reasoning while maintaining competitive efficiency. These capabilities position the system as a foundation for real-time, LLM-assisted knowledge analysis platforms that can support science policy, research evaluation, and meta-scientific inquiry.