Analysis of Computational Science Papers from ICCS 2001-2016 using Topic Modeling and Graph Theory
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.
Cite
@article{arxiv.1705.02203,
title = {Analysis of Computational Science Papers from ICCS 2001-2016 using Topic Modeling and Graph Theory},
author = {Tesfamariam M. Abuhay and Sergey V. Kovalchuk and Klavdiya O. Bochenina and George Kampis and Valeria V. Krzhizhanovskaya and Michael H. Lees},
journal= {arXiv preprint arXiv:1705.02203},
year = {2017}
}
Comments
Accepted by International Conference on Computational Science (ICCS) 2017 which will be held in Zurich, Switzerland from June 11-June 14