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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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Signal Sequences and Sorting Receptors

Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
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Stratified Sampling Method

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Small population sizes put a species at extreme risk of extinction due to a lack of variation, and a consequent decrease in adaptability. This weakens the chances of survival under pressures such as climate change, competition from other species, or new diseases. Large populations are more likely to survive pressures such as these, as such populations are more likely to harbor individuals that have genetic variants that are adaptive under new stresses. Small populations are much less likely to...

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Sequential detection of temporal communities by estrangement confinement.

Vikas Kawadia1, Sameet Sreenivasan

  • 1Raytheon BBN Technologies, Cambridge MA 02138, USA. vkawadia@bbn.com

Scientific Reports
|November 13, 2012
PubMed
Summary

Detecting temporal communities in evolving networks is challenging. A new measure, estrangement, helps find meaningful communities by ensuring partitions remain similar over time.

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Area of Science:

  • Network Science
  • Data Mining
  • Computational Social Science

Background:

  • Temporal communities reveal network evolution dynamics.
  • Existing methods struggle with partition stability over time.
  • Community structures are sensitive to network changes.

Purpose of the Study:

  • To develop a robust method for detecting temporal communities.
  • To address the sensitivity of current methods to network changes.
  • To introduce a new metric for measuring partition similarity over time.

Main Methods:

  • Introduced a novel partition distance measure: estrangement.
  • Constrained estrangement to ensure temporal smoothness.
  • Applied the method to diverse real-world evolving network datasets.

Main Results:

  • Estrangement successfully identifies meaningful temporal communities.
  • The method allows for varying degrees of temporal smoothness.
  • Demonstrated effectiveness across various real-world datasets.

Conclusions:

  • Estrangement confinement offers a principled approach to temporal community detection.
  • This method enhances the reliability of analyzing evolving networks.
  • Provides insights into network cluster dynamics over time.