Publication Type

Journal Article

Version

publishedVersion

Publication Date

1-2017

Abstract

Interest in the habits of influential individuals cuts across domains. As researchers, we are intrigued why few attain significant eminence in their fields, whereas many operate in obscurity. An empirical examination of this question has been made possible by the recent availability of large scale publication data. In this paper, we use information from the AMiner Paper Citation and Author Collaboration Networks to discern factors that relate to the impact of influential researchers across five domains in the computing discipline. We propose and apply a novel algorithm to identify influential vertices in co-authorship networks built from total corpora of 1,00,000+papers and 72,000+authors over a span of more than 50 years. The results from our study indicate that the impact of these influential researchers relate to a variety of factors. Surprisingly, we find evidence across the domains that higher impact is associated with lower levels of collaboration, and authority.

Keywords

Big data, social network analysis, graph algorithms, dominating sets, software engineering, networking, operating systems, databases, artificial intelligence

Discipline

Computer Sciences | Numerical Analysis and Scientific Computing

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE Transactions on Big Data

Volume

3

Issue

1

First Page

3

Last Page

17

ISSN

2332-7790

Identifier

10.1109/TBDATA.2016.2611668

Publisher

IEEE

Embargo Period

6-23-2021

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TBDATA.2016.2611668

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