DocumentCode
3164275
Title
Identifying Transformative Scientific Research
Author
Yi-Hung Huang ; Chun-Nan Hsu ; Lerman, K.
Author_Institution
Dept. of Comput. Sci., Nat. Taiwan Univ., Taipei, Taiwan
fYear
2013
fDate
7-10 Dec. 2013
Firstpage
291
Lastpage
300
Abstract
Transformative research refers to research that shifts or disrupts established scientific paradigms. Notable examples include the discovery of high-temperature superconductivity that disrupted the theory established 30 years ago. Identifying potential transformative research early and accurately is important for funding agencies to maximize the impact of their investments. It also helps scientists identify and focus their attention on promising emerging works. This paper presents a data driven approach where citation patterns of scientific papers are analyzed to quantify how much a potential challenger idea shifts an established paradigm. The key idea is that transformative research creates an observable disruption in the structure of "information cascades," chains of references that can be traced back to the papers establishing some scientific paradigm. Such a disruption is visible soon after the challenger\´s introduction. We define a disruption score to quantify the disruption and develop an algorithm to compute it from a large citation network. Experimental results show that our approach can successfully identify transformative scientific papers that disrupt established paradigms in Physics and Computer Science, regardless of whether the challenger paradigm is an instant hit or a classic whose contribution is formally recognized with a Nobel Prize decades later.
Keywords
citation analysis; data handling; data-driven approach; high-temperature superconductivity; information cascades; large citation network; transformative scientific research identification; Communities; Computer science; Data mining; High-temperature superconductors; Physics; Reliability; cascades; citation network; diffusion; information spread;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Mining (ICDM), 2013 IEEE 13th International Conference on
Conference_Location
Dallas, TX
ISSN
1550-4786
Type
conf
DOI
10.1109/ICDM.2013.120
Filename
6729513
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