TY - GEN
T1 - Trends in Computational Science
T2 - 24th International Conference on Computational Science, ICCS 2024
AU - Luo, Lijing
AU - Kovalchuk, Sergey
AU - Krzhizhanovskaya, Valeria
AU - Paszynski, Maciej
AU - de Mulatier, Clélia
AU - Dongarra, Jack
AU - Sloot, Peter M.A.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - We analyze 7826 publications from the International Conference on Computational Science (ICCS) between 2001 and 2023 using natural language processing and network analysis. We categorize computer science into 13 main disciplines and 102 sub-disciplines sourced from Wikipedia. After lemmatizing full texts of these papers, we calculate the similarity scores between the papers and each sub-discipline using vectors built with TF-IDF evaluation. Among the 13 main disciplines, machine learning & AI have become the most popular topics since 2019, surpassing parallel & distributed computing, which peaked in the early 2010 s. Modeling & simulation, and algorithms & data structure have always been popular disciplines in ICCS over the past 23 years. The most frequently researched sub-disciplines, on average, are algorithms, numerical analysis, and machine learning. Deep learning shows the most rapid growth, while parallel computing has declined over the past 23 years in ICCS publications. The network of sub-disciplines exhibits a scale-free distribution, indicating certain disciplines are more connected than others. We also present correlation analysis of sub-disciplines, both within the same main disciplines and between different main disciplines.
AB - We analyze 7826 publications from the International Conference on Computational Science (ICCS) between 2001 and 2023 using natural language processing and network analysis. We categorize computer science into 13 main disciplines and 102 sub-disciplines sourced from Wikipedia. After lemmatizing full texts of these papers, we calculate the similarity scores between the papers and each sub-discipline using vectors built with TF-IDF evaluation. Among the 13 main disciplines, machine learning & AI have become the most popular topics since 2019, surpassing parallel & distributed computing, which peaked in the early 2010 s. Modeling & simulation, and algorithms & data structure have always been popular disciplines in ICCS over the past 23 years. The most frequently researched sub-disciplines, on average, are algorithms, numerical analysis, and machine learning. Deep learning shows the most rapid growth, while parallel computing has declined over the past 23 years in ICCS publications. The network of sub-disciplines exhibits a scale-free distribution, indicating certain disciplines are more connected than others. We also present correlation analysis of sub-disciplines, both within the same main disciplines and between different main disciplines.
KW - computational science
KW - graph theory
KW - ICCS
KW - natural language processing
KW - network analysis
KW - scientometrics
KW - topic modelling
UR - http://www.scopus.com/inward/record.url?scp=85198154967&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-63751-3_2
DO - 10.1007/978-3-031-63751-3_2
M3 - Conference contribution
AN - SCOPUS:85198154967
SN - 9783031637537
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 19
EP - 33
BT - Computational Science – ICCS 2024 - 24th International Conference, Proceedings
A2 - Franco, Leonardo
A2 - de Mulatier, Clélia
A2 - Paszynski, Maciej
A2 - Krzhizhanovskaya, Valeria V.
A2 - Dongarra, Jack J.
A2 - Sloot, Peter M. A.
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 2 July 2024 through 4 July 2024
ER -