research-article

The Gene of Scientific Success

Authors:

Feng XiaAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 14, Issue 4

Article No.: 41, Pages 1 - 19

https://doi.org/10.1145/3385530

Published: 30 May 2020 Publication History

Abstract

This article elaborates how to identify and evaluate causal factors to improve scientific impact. Currently, analyzing scientific impact can be beneficial to various academic activities including funding application, mentor recommendation, discovering potential cooperators, and the like. It is universally acknowledged that high-impact scholars often have more opportunities to receive awards as an encouragement for their hard work. Therefore, scholars spend great efforts in making scientific achievements and improving scientific impact during their academic life. However, what are the determinate factors that control scholars’ academic success? The answer to this question can help scholars conduct their research more efficiently. Under this consideration, our article presents and analyzes the causal factors that are crucial for scholars’ academic success. We first propose five major factors including article-centered factors, author-centered factors, venue-centered factors, institution-centered factors, and temporal factors. Then, we apply recent advanced machine learning algorithms and jackknife method to assess the importance of each causal factor. Our empirical results show that author-centered and article-centered factors have the highest relevancy to scholars’ future success in the computer science area. Additionally, we discover an interesting phenomenon that the h-index of scholars within the same institution or university are actually very close to each other.

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Index Terms

The Gene of Scientific Success
1. Information systems
  1. Information systems applications
2. Social and professional topics
  1. Professional topics

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 14, Issue 4

August 2020

316 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3403605

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
Minginglamp Academy of Sciences, China

Issue’s Table of Contents

Copyright © 2020 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 May 2020

Online AM: 07 May 2020

Accepted: 01 February 2020

Revised: 01 December 2019

Received: 01 October 2018

Published in TKDD Volume 14, Issue 4

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https://dl.acm.org/doi/10.1016/j.eswa.2022.118841
Liang GLi YSong LHuo C(2023)Magnitude decrease of the Matthew effect in citations: a study based on Nobel Prize articlesScientometrics10.1007/s11192-023-04874-4128:12(6357-6371)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s11192-023-04874-4
Shen GTan JLiu ZKong X(2022)Enhancing interactive graph representation learning for review-based item recommendationComputer Science and Information Systems10.2298/CSIS210228064S19:2(573-593)Online publication date: 2022
https://doi.org/10.2298/CSIS210228064S
Tsugawa SKanetsuki TSugihara J(2022)Relationship between early-career collaboration among researchers and future funding success in Japanese academiaPLOS ONE10.1371/journal.pone.027762117:11(e0277621)Online publication date: 11-Nov-2022
https://doi.org/10.1371/journal.pone.0277621
Liu JXia FFeng XRen JLiu H(2022)Deep Graph Learning for Anomalous Citation DetectionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.314509233:6(2543-2557)Online publication date: Jun-2022
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