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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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Generalized network dismantling based on cost-aware source-sampling betweenness.

Jihui Han1, Chengyi Zhang1, Gaogao Dong2

  • 1School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450000, Henan, China.

Chaos (Woodbury, N.Y.)
|May 2, 2025
PubMed
Summary
This summary is machine-generated.

We developed a new algorithm, Cost-Aware Source-Sampling Betweenness (CASS-Bet), for efficient network dismantling. This method balances node importance and removal costs, proving superior for infrastructure protection and crime control.

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Area of Science:

  • Network Science
  • Computer Science
  • Applied Mathematics

Background:

  • Network dismantling is crucial for critical applications like infrastructure protection and epidemic control.
  • Current methods often struggle with efficiency and cost-awareness in real-world scenarios.

Purpose of the Study:

  • To introduce a novel, scalable algorithm for cost-aware network dismantling.
  • To dynamically balance node importance and removal costs for optimized network disruption.

Main Methods:

  • Developed the Cost-Aware Source-Sampling Betweenness (CASS-Bet) algorithm.
  • Implemented dynamic node prioritization based on real-time network changes.
  • Utilized a scalable sampling technique for computational efficiency.

Main Results:

  • CASS-Bet demonstrated superior performance across social, infrastructure, and criminal networks.
  • The algorithm enables cost-effective network dismantling with minimal resource expenditure.
  • Achieved high computational efficiency and flexibility in defining removal costs.

Conclusions:

  • CASS-Bet offers a practical and scalable solution for real-world network dismantling challenges.
  • The algorithm enhances infrastructure resilience and aids in disrupting organized crime networks.
  • Provides a flexible framework adaptable to various practical cost constraints.