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Adaptive robust structure exploration for complex systems based on model configuration and fusion.

Yingfei Qu1, Wanbing Liu2, Junhao Wen1

  • 1Computer Science and Technology Post-Doctoral Station, Chongqing University, Chongqing, China.

Peerj. Computer Science
|April 25, 2024
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Summary
This summary is machine-generated.

This study introduces an adaptive algorithm using a generative model to explore complex system structures. It optimizes node parameters to reveal latent characteristics, overcoming limitations of traditional methods for better insights.

Keywords:
Algorithm fusionComplex networkComplex systemModel configurationMultiple structural features

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Area of Science:

  • Network Science
  • Computational Social Science
  • Data Mining

Background:

  • Analyzing complex systems is difficult, with traditional methods often yielding biased conclusions by focusing narrowly on structures like communities.
  • Existing approaches may miss crucial features of complex systems due to their limited scope.

Purpose of the Study:

  • To propose an adaptive algorithm for exploring complex system structures.
  • To utilize a generative model for calculating and optimizing node parameters that represent latent structural characteristics.
  • To demonstrate the algorithm's effectiveness and adaptability on benchmark and real-world networks.

Main Methods:

  • Development of an adaptive algorithm employing a generative model.
  • Optimization of node parameters to capture underlying system structures.
  • Algorithm fusion strategies tested for enhanced adaptability.
  • Comparative experiments on 10 benchmark networks and two real-world networks.

Main Results:

  • The proposed method effectively calculates and optimizes node parameters reflecting latent structural features.
  • Experiments on benchmark networks confirm the method's effectiveness and stability.
  • Tests on real-world networks show the algorithm can uncover diverse structural features like clustering, overlapping, and local chaining.

Conclusions:

  • The adaptive algorithm offers a robust and promising approach for analyzing complex system structures.
  • This method provides a more comprehensive understanding of system features beyond traditional community detection.
  • The generative model-based approach enhances the exploration of complex network properties.