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Related Concept Videos

Sampling Plans01:23

Sampling Plans

181
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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Convenience Sampling Method00:55

Convenience Sampling Method

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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.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
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Sampling Methods: Overview01:06

Sampling Methods: Overview

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
315
Systematic Sampling Method01:17

Systematic Sampling Method

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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.
Systematic sampling is one of the simplest methods...
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Random Sampling Method01:09

Random Sampling Method

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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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Stratified Sampling Method01:16

Stratified Sampling Method

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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. 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.
To choose a stratified sample, divide the population into groups called strata and then take a...
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An Unbiased Approach of Sampling TEM Sections in Neuroscience
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Towards a better negative sampling strategy for dynamic graphs.

Kuang Gao1, Chuang Liu1, Jia Wu2

  • 1School of Computer Science, Wuhan University, China.

Neural Networks : the Official Journal of the International Neural Network Society
|February 22, 2024
PubMed
Summary
This summary is machine-generated.

Enhanced Negative Sampling (ENS) improves Temporal Graph Neural Networks (TGNNs) for dynamic graph link prediction by balancing sample difficulty. This approach enhances model generalization and performance on evolving network data.

Keywords:
Curriculum learningDynamic graphNegative sampling

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Area of Science:

  • Graph Neural Networks
  • Machine Learning
  • Network Science

Background:

  • Dynamic graphs are crucial for modeling evolving relationships.
  • Temporal Graph Neural Networks (TGNNs) are increasingly used for dynamic graph analysis.
  • Standard negative sampling in TGNNs can lead to overfitting and poor generalization.

Purpose of the Study:

  • To introduce an innovative negative sampling method, Enhanced Negative Sampling (ENS), for TGNNs.
  • To address the limitations of conventional negative sampling in dynamic graph link prediction.
  • To improve the generalization ability of TGNNs on dynamic graph data.

Main Methods:

  • Developed Enhanced Negative Sampling (ENS) considering historical dependence and temporal proximity.
  • Implemented a scheduling function to control the difficulty progression of negative samples during training.
  • Integrated ENS as a modular component with four state-of-the-art (SOTA) baselines.

Main Results:

  • ENS significantly improved the performance of four SOTA TGNN baselines across multiple datasets.
  • The proposed method demonstrated effectiveness in handling dynamic graphs with varied attributes.
  • Empirical evaluations confirmed the augmented performance of TGNNs using ENS.

Conclusions:

  • Enhanced Negative Sampling (ENS) is an effective strategy for improving TGNN performance in dynamic graph link prediction.
  • The method's ability to balance sample difficulty enhances model generalization.
  • ENS offers a valuable, modular enhancement for existing TGNN architectures.