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Related Experiment Video

Updated: Feb 6, 2026

Generating a Fractal Microstructure of Laminin-111 to Signal to Cells
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Dismantling efficiency and network fractality.

Yoon Seok Im1, B Kahng1

  • 1CCSS, CTP and Department of Physics and Astronomy, Seoul National University, Seoul 08826, Korea.

Physical Review. E
|August 17, 2018
PubMed
Summary

The belief propagation-based decimation (BPD) algorithm is more effective for network dismantling than the collective influence (CI) algorithm, regardless of network type. CI performs better on nonfractal networks.

Area of Science:

  • Network Science
  • Complex Systems
  • Statistical Physics

Background:

  • Network dismantling aims to identify critical nodes for breaking networks into smaller components.
  • Finding optimal node sets is NP-hard, necessitating heuristic algorithms like Belief Propagation-based Decimation (BPD) and Collective Influence (CI).

Purpose of the Study:

  • To evaluate and compare the performance of BPD and CI algorithms in network dismantling.
  • To analyze algorithm performance based on network fractality and structural features.

Main Methods:

  • Testing BPD and CI algorithms on fractal and nonfractal network models.
  • Constructing diverse model networks by varying degree exponent, shortcut number, and system size.
  • Analyzing algorithm efficiency in relation to network fractality and structural properties.

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Main Results:

  • The BPD algorithm consistently outperforms the CI algorithm across both fractal and nonfractal networks.
  • The CI algorithm demonstrates superior performance on nonfractal networks compared to fractal ones.
  • Algorithm performance is shown to be dependent on specific network structural features.

Conclusions:

  • BPD is a more robust and generally efficient heuristic for network dismantling.
  • Network structure, particularly fractality, significantly influences the effectiveness of different dismantling algorithms.
  • Understanding these dependencies can guide the selection of appropriate algorithms for specific network types.