Munjung Kim

PhD Student in Informatics (Complex Networks and Systems) · Indiana University Bloomington
munjkim@iu.edu

Hi! I'm a PhD student in Informatics at Indiana University Bloomington, specializing in Complex Systems and Network Science. My research focuses on using and developing advanced AI and data science techniques to understand innovation in science and technology. Thesedays, I'm also interested in exploring how large language models can be enhanced and understood through the lens of collective intelligence. Before joining the PhD program, I completed my undergraduate studies in Physics at Pohang University of Science and Technology (POSTECH).

Machine Learning · Large Language Models · Science of Science

Projects & Papers

Quantifying Disruptiveness using neural embedding method

Although understanding disruptive breakthroughs and their drivers hinges upon accurately quantifying disruptiveness, the core metric used in previous studies—the disruption index—remains insufficiently understood and tested. Here, after demonstratinits conflicting evaluations for simultaneous discoveries or “multiples”, we propose a new, continuous measure of disruptiveness based on a neural embedding framework that addresses these limitations. Our measure not only better distinguishes disruptive works, such as Nobel Prize-winning papers, from others, but also reveals simultaneous disruptions by allowing us to examine the “twins” that have the most similar future context. By offering a more robust and precise lens for identifying disruptive innovations and simultaneous discoveries, our study provides a foundation for deepening insights into the mechanisms driving scientific breakthroughs while establishing a more equitable basis for evaluating transformative contributions.

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Quantifying topic diversity and disparity using neural embedding methods

Citation count is a popular index for assessing scientific papers. However, it depends on not only the quality of a paper but also various factors, such as conventionality, journal, team size, career age, and gender. Here, we examine the extent to which the conventionality of a paper is related to its citation count by using our measure, topic disparity. The topic disparity is the cosine distance between a paper and its discipline on a neural embedding space. Using this measure, we show that the topic disparity is negatively associated with citation count, even after controlling journal impact, team size, and the career age and gender of the first and last authors. This result indicates that less conventional research tends to receive fewer citations than conventional research. The topic disparity can be used to complement citation count and to recommend papers at the periphery of a discipline because of their less conventional topics.

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Munjung Kim

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Projects & Papers

Quantifying Disruptiveness using neural embedding method

Quantifying topic diversity and disparity using neural embedding methods

Education

Indiana University

Pohang University of Science and Technology

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Awards & Certifications