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Updated: Jan 29, 2026

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses
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A Prospective Method for the Dynamic Transformation of Structural Balance in Fully Signed Networks.

Zhanyong Jiao1, Jiarui Fan2, Ruochen Zhang3

  • 1School of Marxism, Xi'an Jiaotong University, Xi'an 710049, China.

Entropy (Basel, Switzerland)
|January 28, 2026
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This study introduces a new method to understand how social networks change over time by including individual characteristics. It optimizes network balance efficiently, offering insights into complex social dynamics.

Keywords:
computational social sciencememetic algorithmnetwork optimizationsigned networksstructural balance theory

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Area of Science:

  • Social Computing
  • Network Science
  • Computational Social Science

Background:

  • Structural balance theory is crucial for understanding social networks.
  • Dynamic transformations in signed networks, considering node attributes, are underexplored.
  • Existing models often lack integration of individual attributes into dynamic balance analysis.

Purpose of the Study:

  • Extend structural balance theory to fully signed networks with node attributes.
  • Develop a mathematical framework for optimizing balance dynamics.
  • Propose an efficient algorithm for achieving structural balance.

Main Methods:

  • Formulated a mathematical framework for balance dynamics.
  • Designed a memetic algorithm for cost-effective structural balance.
  • Incorporated node attributes into the balance optimization process.

Main Results:

  • Demonstrated effectiveness on synthetic and real-world networks.
  • Showcased the efficiency of the proposed memetic algorithm.
  • Validated the social interpretability of the balance optimization method.

Conclusions:

  • The proposed framework effectively models dynamic structural balance in signed networks with attributes.
  • The memetic algorithm provides an efficient solution for achieving balance.
  • This research offers valuable insights for social computing and network analysis.