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This paper presents results of topic modeling and network models of topicsusing the International Conference on Computational Science corpus, whichcontains domain-specific (computational science) papers over sixteen years (atotal of 5695 papers). We discuss topical structures of InternationalConference on Computational Science, how these topics evolve over time inresponse to the topicality of various problems, technologies and methods, andhow all these topics relate to one another. This analysis illustratesmultidisciplinary research and collaborations among scientific communities, byconstructing static and dynamic networks from the topic modeling results andthe keywords of authors. The results of this study give insights about the pastand future trends of core discussion topics in computational science. We usedthe Non-negative Matrix Factorization topic modeling algorithm to discovertopics and labeled and grouped results hierarchically.

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