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Measuring multiple evolution mechanisms of complex networks.

Qian-Ming Zhang1, Xiao-Ke Xu2, Yu-Xiao Zhu3

  • 11] Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China [2] Center for Polymer Studies, Department of Physics, Boston University, Boston 02215, United States of America [3] Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.

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Summary
This summary is machine-generated.

Real-world networks evolve through multiple mechanisms. This study introduces likelihood analysis to accurately measure these complex network evolution drivers, revealing how popularity and clustering co-evolve in diverse networks.

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Area of Science:

  • Complex networks analysis
  • Network evolution modeling
  • Data science

Background:

  • Real-world networks are typically shaped by multiple interacting evolution mechanisms, not single drivers.
  • Existing hybrid models offer limited insight into the joint influence of multiplex features on network evolution.
  • Accurate simulation of complex networks necessitates understanding these combined evolutionary forces.

Purpose of the Study:

  • To introduce and evaluate methods for measuring multiple evolution mechanisms in complex networks.
  • To determine the effectiveness of link prediction versus likelihood analysis for quantifying network evolution.
  • To investigate the co-evolution of popularity and clustering in real-world networks.

Main Methods:

  • Development of two quantitative methods: link prediction and likelihood analysis.
  • Extensive experiments on artificial networks with controlled evolutionary mechanism weights.
  • Application of the superior method to diverse real-world technological and social networks.

Main Results:

  • Likelihood analysis significantly outperforms link prediction in accurately estimating the weights of multiple evolution mechanisms.
  • Experiments on artificial networks validate the precision of the likelihood analysis method.
  • Real-world networks from technology and social domains show significant influence from both popularity and clustering, with varying contribution weights.

Conclusions:

  • Likelihood analysis provides a robust and accurate approach for dissecting the interplay of multiple evolution mechanisms in complex networks.
  • Popularity and clustering are key co-evolving features across various real-world network types.
  • The relative importance of popularity and clustering varies considerably across different network domains and geographical origins.