Predicting the evolution of physics research from a complex network perspective
The advancement of science, as outlined by Popper and Kuhn, is largely qualitative, but with bibliometric data, it is possible and desirable to develop a quantitative picture of scientific progress. Furthermore, it is also important to allocate finite resources to research topics that have the growt...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Online Access: | https://hdl.handle.net/10356/155409 |
| _version_ | 1811692963197091840 |
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| author | Liu, Wenyuan Saganowsk, Stanislaw Kazienko, Przemysław Cheong, Siew Ann |
| author2 | School of Physical and Mathematical Sciences |
| author_facet | School of Physical and Mathematical Sciences Liu, Wenyuan Saganowsk, Stanislaw Kazienko, Przemysław Cheong, Siew Ann |
| author_sort | Liu, Wenyuan |
| collection | NTU |
| description | The advancement of science, as outlined by Popper and Kuhn, is largely qualitative, but with bibliometric data, it is possible and desirable to develop a quantitative picture of scientific progress. Furthermore, it is also important to allocate finite resources to research topics that have the growth potential to accelerate the process from scientific breakthroughs to technological innovations. In this paper, we address this problem of quantitative knowledge evolution by analyzing the APS data sets from 1981 to 2010. We build the bibliographic coupling and co-citation networks, use the Louvain method to detect topical clusters (TCs) in each year, measure the similarity of TCs in consecutive years, and visualize the results as alluvial diagrams. Having the predictive features describing a given TC and its known evolution in the next year, we can train a machine learning model to predict future changes of TCs, i.e., their continuing, dissolving, merging, and splitting. We found the number of papers from certain journals, the degree, closeness, and betweenness to be the most predictive features. Additionally, betweenness increased significantly for merging events and decreased significantly for splitting events. Our results represent the first step from a descriptive understanding of the science of science (SciSci), towards one that is ultimately prescriptive. |
| first_indexed | 2024-10-01T06:44:08Z |
| format | Journal Article |
| id | ntu-10356/155409 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:44:08Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1554092023-02-28T19:56:58Z Predicting the evolution of physics research from a complex network perspective Liu, Wenyuan Saganowsk, Stanislaw Kazienko, Przemysław Cheong, Siew Ann School of Physical and Mathematical Sciences Complexity Institute Science::Physics Knowledge Evolution Machine Learning The advancement of science, as outlined by Popper and Kuhn, is largely qualitative, but with bibliometric data, it is possible and desirable to develop a quantitative picture of scientific progress. Furthermore, it is also important to allocate finite resources to research topics that have the growth potential to accelerate the process from scientific breakthroughs to technological innovations. In this paper, we address this problem of quantitative knowledge evolution by analyzing the APS data sets from 1981 to 2010. We build the bibliographic coupling and co-citation networks, use the Louvain method to detect topical clusters (TCs) in each year, measure the similarity of TCs in consecutive years, and visualize the results as alluvial diagrams. Having the predictive features describing a given TC and its known evolution in the next year, we can train a machine learning model to predict future changes of TCs, i.e., their continuing, dissolving, merging, and splitting. We found the number of papers from certain journals, the degree, closeness, and betweenness to be the most predictive features. Additionally, betweenness increased significantly for merging events and decreased significantly for splitting events. Our results represent the first step from a descriptive understanding of the science of science (SciSci), towards one that is ultimately prescriptive. Ministry of Education (MOE) Published version This research was funded by the Singapore Ministry of Education Academic Research Fund Tier 2 under Grant Number MOE2017-T2-2-075, the National Science Centre, Poland, Project No. 2016/21/B/ST6/01463, the European Union’s Marie Skłodowska-Curie Program under Grant Agreement No. 691152 (RENOIR), and the Polish Ministry of Science and Higher Education under Grant Agreement No. 3628/H2020/2016/2, and the statutory funds of the Department of Computational Intelligence, Wrocław University of Science and Technology 2022-02-25T07:59:38Z 2022-02-25T07:59:38Z 2019 Journal Article Liu, W., Saganowsk, S., Kazienko, P. & Cheong, S. A. (2019). Predicting the evolution of physics research from a complex network perspective. Entropy, 21(12), 1152-. https://dx.doi.org/10.3390/e21121152 1099-4300 https://hdl.handle.net/10356/155409 10.3390/e21121152 2-s2.0-85079170486 12 21 1152 en MOE2017-T2-2-075 Entropy © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). application/pdf |
| spellingShingle | Science::Physics Knowledge Evolution Machine Learning Liu, Wenyuan Saganowsk, Stanislaw Kazienko, Przemysław Cheong, Siew Ann Predicting the evolution of physics research from a complex network perspective |
| title | Predicting the evolution of physics research from a complex network perspective |
| title_full | Predicting the evolution of physics research from a complex network perspective |
| title_fullStr | Predicting the evolution of physics research from a complex network perspective |
| title_full_unstemmed | Predicting the evolution of physics research from a complex network perspective |
| title_short | Predicting the evolution of physics research from a complex network perspective |
| title_sort | predicting the evolution of physics research from a complex network perspective |
| topic | Science::Physics Knowledge Evolution Machine Learning |
| url | https://hdl.handle.net/10356/155409 |
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