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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

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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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Stepwise Cell Seeding on Tessellated Scaffolds to Study Sprouting Blood Vessels
07:49

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Balancing Speed and Coverage by Sequential Seeding in Complex Networks.

Jarosław Jankowski1,2, Piotr Bródka3, Przemysław Kazienko3

  • 1Department of Computational Intelligence, ENGINE - The European Centre for Data Science, Wrocław University of Science and Technology, Wrocław, 50-370, Poland. jjankowski@wi.zut.edu.pl.

Scientific Reports
|April 20, 2017
PubMed
Summary
This summary is machine-generated.

Sequential seeding strategies improve information diffusion in complex networks by activating nodes in stages, leading to better coverage in most cases compared to simultaneous seeding.

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Area of Science:

  • Network Science
  • Information Diffusion Models

Background:

  • Information spreading in complex networks is commonly modeled as diffusion from activated nodes to neighbors.
  • Information cascades occur when initial seed nodes trigger widespread diffusion.

Purpose of the Study:

  • To introduce and evaluate novel sequential seed initiation strategies for information diffusion.
  • To compare sequential seeding with traditional single-stage seeding approaches.

Main Methods:

  • Developed sequential seeding strategies that activate nodes in stages, avoiding already diffused nodes.
  • Compared sequential and single-stage seeding using various ranking methods and diffusion parameters on real networks.

Main Results:

  • Sequential seeding strategies outperformed single-stage seeding, achieving better coverage in approximately 90% of tested scenarios.
  • Longer seeding sequences increased activated nodes but also extended diffusion time.
  • Different sequential variants offered varied trade-offs between coverage and diffusion speed.

Conclusions:

  • Sequential seeding is a more effective approach for maximizing information diffusion coverage in complex networks.
  • The timing of seed activation significantly impacts the efficiency and outcome of information cascades.
  • Optimizing sequential seeding strategies can balance network coverage with diffusion speed.