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

This paper presents results of topic modeling and network models of topics using the ICCS corpus, which contains domain-specific (computational science) papers over sixteen years (a total of 5695 papers). We discuss topical structures of ICCS, how these topics evolve over time in response to the top...

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Bibliographic Details
Published in:Procedia computer science 2017, Vol.108, p.7-17
Main Authors: Abuhay, Tesfamariam M., Kovalchuk, Sergey V., Bochenina, Klavdiya O., Kampis, George, Krzhizhanovskaya, Valeria V., Lees, Michael H.
Format: Article
Language:English
Subjects:
computational science
graph theory
ICCS
natural language processing
topic modeling
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Summary:This paper presents results of topic modeling and network models of topics using the ICCS corpus, which contains domain-specific (computational science) papers over sixteen years (a total of 5695 papers). We discuss topical structures of ICCS, 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 authors’ keywords. 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(NMF) topic modeling algorithm to discover topics and labeled and grouped results hierarchically. We used Gephi to study static networks of topics, and an R library called DyA to analyze the dynamic networks of topics.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2017.05.183