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Is scientific novelty reflected in citation patterns?
Author(s) -
Min Chao,
Bu Yi,
Sun Jianjun,
Ding Ying
Publication year - 2018
Publication title -
proceedings of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.193
H-Index - 14
ISSN - 2373-9231
DOI - 10.1002/pra2.2018.14505501155
Subject(s) - novelty , cluster analysis , citation , computer science , diffusion , process (computing) , data science , clustering coefficient , scientific literature , information retrieval , data mining , artificial intelligence , psychology , world wide web , physics , geology , social psychology , paleontology , thermodynamics , operating system
We investigate the diffusion characteristics of two groups of scientific works with different levels of novelty to see how scientific novelty interacts with citation patterns. Preliminary results show that: (1) novel ideas have long‐lasting impact, but the recognition process lags behind; (2) most diffusion metrics can't differentiate the two groups under a similar macro environment; (3) citation take‐off, average clustering coefficient, and connectivity can distinguish works with different levels of novelty. The latter two characteristics—average clustering coefficient and connectivity of citing literature networks—show particular potential for identifying innovative scientific works at an early stage.