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Title:Discover citation topic distribution patterns of highly cited papers
Author(s):Wu, Sizhu; Ding, Ying; Xu, Jian
Subject(s):Pattern discovery
Citation topic distribution
Impact research
Abstract:Highly cited researches represent most influential scientific minds in the world that are highly regarded by many researchers. “Dwarfs standing on the shoulders of giants”, but where the giants are standing on remains unclear. In this study, we have selected 468787 research publications in computer science in ArnetMiner and analyzed the citation topic distribution to observe how and to what extent prior work is combined. We have found that there is a novelty and conventionality combination in different impact papers at reference topic level, but their features are distinct. Our result shows that highly cited papers have more novel combination than middle and lowly cited papers relatively in reference topic pairs and there is a remarkable variation range for this phenomenon.
Issue Date:2017
Publisher:iSchools
Citation Info:Wu, S., Ding, Y., & Xu, J. (2017). Discover Citation Topic Distribution Patterns of Highly Cited Papers. In iConference 2017 Proceedings (pp. 739-743). https://doi.org/10.9776/17331
Series/Report:iConference 2017 Proceedings
Genre:Conference Poster
Type:Text
Language:English
URI:http://hdl.handle.net/2142/96710
DOI:https://doi.org/10.9776/17331
Rights Information:Copyright 2017 Sizhu Wu, Ying Ding, and Jian Xu
Date Available in IDEALS:2017-07-27


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