تجزیه و تحلیل فعالیت انتشارات جامعه علمی محاسباتی در سالهای 2001 تا 2017 با استفاده از مدل سازی موضوعی و نظریه گراف

This paper presents results of topic modeling and network models of topics using the ICCS corpus, which contains domain-specific (computational science) papers over seventeen years (a total of 5982 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 and how “computational thinking” propagates into different fields of study. We used the Non-negative Matrix Factorization (NMF) topic modeling algorithm to discover topics. The results were labeled and grouped hierarchically. The hierarchy of topics was developed by a manual analysis of topics and includes three levels. We applied trend analysis and Change Point Analysis (CPA) to identify growing and disappearing topics and study evolution of topics over seventeen years. We used Gephi to study static networks of topics, and an R library called DyA to analyze the dynamic networks of topics. We also analyzed the conference as a platform for potential collaboration development through the perspective of collaboration networks. It occurred that authors of ICCS papers continue to actively collaborate after the conference − on the average authors collaborate with three other authors, − which suggests that ICCS is a valuable platform for collaboration development.