Analysis of Computational Science Papers from ICCS 2001-2016 using Topic Modeling and Graph Theory

TL;DR

This study analyzes 16 years of computational science papers from ICCS using topic modeling and network analysis to reveal evolving research trends, interdisciplinary collaborations, and future directions in the field.

Contribution

It applies NMF-based topic modeling and network analysis to a large corpus, providing insights into the evolution and interrelation of research topics in computational science.

Findings

Identifies key research topics and their evolution over 16 years.

Reveals interdisciplinary collaborations among scientific communities.

Provides insights into future trends in computational science.

Abstract

This paper presents results of topic modeling and network models of topics using the International Conference on Computational Science corpus, which contains domain-specific (computational science) papers over sixteen years (a total of 5695 papers). We discuss topical structures of International Conference on Computational Science, how these topics evolve over time in response to the topicality of various problems, technologies and methods, and how all these topics relate to one another. This analysis illustrates multidisciplinary research and collaborations among scientific communities, by constructing static and dynamic networks from the topic modeling results and the keywords of authors. The results of this study give insights about the past and future trends of core discussion topics in computational science. We used the Non-negative Matrix Factorization topic modeling algorithm to discover topics and labeled and grouped results hierarchically.