Integrating semantic clustering and citation analysis to construct a topic citation network for characterizing scholars’ contributions: A case study of price prize laureates

Abstract

This study proposes a topic citation network framework that integrates semantic clustering and citation analysis to systematically characterize the evolution of knowledge sources, research topics, and academic contributions of scholars over time. Leveraging data from OpenAlex, we compile the publication and citation records of 32 Price Prize laureates, resulting in a dataset of 135,066 unique documents. Using this dataset as a case study, we employ Sentence-BERT for semantic modeling of document titles and K-means clustering for topic segmentation, thereby constructing a topic citation network encompassing 54 distinct research topics. Through this framework, the temporal evolution of key research topics is examined across multiple dimensions, including publication trends, citation patterns, and topic evolution paths. The findings reveal that Price Prize laureates exhibit sustained engagement with core research areas, fostering the formation of stable and influential knowledge dissemination networks. Their academic output and impact typically peak during the mid-career stage, with their contributions demonstrating significant long-term influence. Moreover, the continuity and transfer of knowledge among scholars tend to concentrate around specific research topics, underscoring the cumulative and structured nature of scientific knowledge dissemination. These insights highlight the pivotal role of Price Prize laureates in shaping the field of scientometrics and emphasize the value of the proposed framework as a systematic approach for evaluating scholarly contributions.