Discover citation topic distribution patterns of highly cited papers

Wu, Sizhu; Ding, Ying; Xu, Jian

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https://hdl.handle.net/2142/96710 Copy

Description

Title

Discover citation topic distribution patterns of highly cited papers

Author(s)

• Wu, Sizhu
• Ding, Ying
• Xu, Jian

Issue Date

2017

Keyword(s)

• Pattern discovery
• Citation topic distribution
• Impact research

Date of Ingest

2017-07-27T15:57:51Z

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.

Publisher

iSchools

Series/Report Name or Number

iConference 2017 Proceedings

Type of Resource

text

Genre of Resource

Conference Poster

Language

en

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http://hdl.handle.net/2142/96710

Copyright and License Information

Copyright 2017 Sizhu Wu, Ying Ding, and Jian Xu

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