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World Wide Web

, Volume 22, Issue 6, pp 2749–2770 | Cite as

CSTeller: forecasting scientific collaboration sustainability based on extreme gradient boosting

  • Wei Wang
  • Bo XuEmail author
  • Jiaying Liu
  • Zixin Cui
  • Shuo Yu
  • Xiangjie Kong
  • Feng Xia
Article
First Online:
Part of the following topical collections:
  1. Special Issue on Social Computing and Big Data Applications

Abstract

The mechanism why two strange scholars become collaborators has been extensively studied from the perspective of social network analysis. In academia, two scholars may collaborate with each other more than once, which means that scientific collaboration is to some extent sustainable. However, less research has been done to explore the sustainability of scientific collaboration. In this paper, we examine to what extent the collaboration sustainability can be predicted. For this purpose, an extreme gradient boosting-based collaboration sustainability prediction model named CSTeller is devised. We propose to analyze the sustainability of scientific collaboration from the perspectives of collaboration duration and collaboration times. We investigate factors that may affect collaboration sustainability based on scholars’ local properties and network properties. These factors are adopted as input features of CSTeller. Extensive experiments on two real scholarly datasets demonstrate the effectiveness of our proposed model. To the best of our knowledge, this is the first attempt to explore scientific collaboration mechanism from the perspective of sustainability. Our work may shed light on scientific collaboration analysis and benefit many practical issues such as collaborator recommendation since a scientific collaboration is not a one-shot deal.

Keywords

Scholarly big data Deep learning Relation mining Coauthor network 
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Notes

Acknowledgements

We thank Tong Gao for assistance with the experiments. This work was supported by the National Natural Science Foundation of China (NSFC) under Grant 61502071, 71774020 and 71473028, and the Fundamental Research Funds for the Central Universities under Grant (DUT18JC09).

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Authors and Affiliations

  1. 1.Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of SoftwareDalian University of TechnologyDalianChina

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