  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.
