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Identification of Potential Cooperation Relationships Among Scientists.

Fuzhong Nian1, Yinuo Qian1, Yabing Yao1

  • 1School of Computer and Communication, Lanzhou University of Technology, Lanzhou, China.

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|September 9, 2022
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Summary

This study enhances scientist cooperation networks by simulating information spread and improving link prediction algorithms. A hybrid approach combining node attributes and spread factors accurately identifies potential collaborations.

Keywords:
information spreadlink predictionnode attributesscientist cooperation network

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Area of Science:

  • Network Science
  • Bibliometrics
  • Computational Social Science

Background:

  • Scientist cooperation networks are crucial for research advancement.
  • Understanding information spread dynamics is key to network evolution.
  • Link prediction aids in identifying potential collaborations.

Purpose of the Study:

  • To analyze information spread in scientist cooperation networks.
  • To develop an improved link prediction algorithm for these networks.
  • To identify potential collaboration opportunities for scientists.

Main Methods:

  • Abstracting real networks into simulated networks.
  • Simulating information spread using an improved SIS model.
  • Developing a hybrid weighted link prediction algorithm incorporating node attributes and spread factors.

Main Results:

  • The improved SIS model effectively simulates information spread in cooperation networks.
  • The hybrid weighted link prediction algorithm significantly improves prediction accuracy.
  • Experimental results validate the propagation model and link prediction algorithm on both simulated and real networks.

Conclusions:

  • Information spread analysis reveals network formation laws.
  • Link prediction methods preserve network information integrity.
  • The hybrid algorithm offers practical suggestions for scientists seeking partners, enriching cooperation networks.