research-article

Scientific Collaboration Sustainability Prediction Based on H-index Reciprocity

Authors:

Wei Wang,

Junyang Chen,

Weiwei Sun,

Zhiguo GongAuthors Info & Claims

WWW '20: Companion Proceedings of the Web Conference 2020

Pages 71 - 72

https://doi.org/10.1145/3366424.3382702

Published: 20 April 2020 Publication History

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Abstract

Despite the increasing interest to investigate and promote scientific collaborations, we know little about scientific collaboration sustainability. In this work-in-progress, we propose to investigate the extent to which scientific collaboration sustainability can be predicted. For this purpose, we design a reciprocity-based collaboration sustainability prediction model by injecting h-index reciprocity. Experiments indicate considering early-stage reciprocity can improve prediction performance compared with state-of-the-art methods.

References

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David Liben-Nowell and Jon Kleinberg. 2007. The link-prediction problem for social networks. journal of the Association for Information Science and Technology 58, 7(2007), 1019–1031.

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Roberta Sinatra, Dashun Wang, Pierre Deville, Chaoming Song, and Albert-László Barabási. 2016. Quantifying the evolution of individual scientific impact. Science 354, 6312 (2016), aaf5239.

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[3]

Wei Wang, Zixin Cui, Tong Gao, Shuo Yu, Xiangjie Kong, and Feng Xia. 2017. Is Scientific Collaboration Sustainability Predictable?. In Proceedings of the 26th International Conference on World Wide Web Companion. International World Wide Web Conferences Steering Committee, 853–854.

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Xu BGuo JLiu S(2024)Graph-based algorithm for exploring collaboration mechanisms and hidden patterns among top scholarsExpert Systems with Applications10.1016/j.eswa.2024.123810249(123810)Online publication date: Sep-2024
https://doi.org/10.1016/j.eswa.2024.123810
Qiao ZFu YWang PXiao MNing ZZhang DDu YZhou Y(2023)RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-TrainingIEEE Transactions on Big Data10.1109/TBDATA.2022.31523869:1(186-199)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TBDATA.2022.3152386

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Scientific Collaboration Sustainability Prediction Based on H-index Reciprocity

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Published In

WWW '20: Companion Proceedings of the Web Conference 2020

April 2020

854 pages

ISBN:9781450370240

DOI:10.1145/3366424

Editors:
Amal El Fallah Seghrouchni
Sorbonne University, France
,
Gita Sukthankar
University of Central Florida, United States
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 20 April 2020

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WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

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Xu BGuo JLiu S(2024)Graph-based algorithm for exploring collaboration mechanisms and hidden patterns among top scholarsExpert Systems with Applications10.1016/j.eswa.2024.123810249(123810)Online publication date: Sep-2024
https://doi.org/10.1016/j.eswa.2024.123810
Qiao ZFu YWang PXiao MNing ZZhang DDu YZhou Y(2023)RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-TrainingIEEE Transactions on Big Data10.1109/TBDATA.2022.31523869:1(186-199)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TBDATA.2022.3152386

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A bibliometric analysis of highly cited papers in the field of Economics and Business based on the Essential Science Indicators database

Scientific Quality Index: a composite size-independent metric compared with h-index for 480 medical researchers

Detecting h-index manipulation through self-citation analysis

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