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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.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Sampling Plans01:23

Sampling Plans

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...
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
Sampling Methods: Overview01:06

Sampling Methods: Overview

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 sampling...
Random Sampling Method01:09

Random Sampling Method

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...
Systematic Sampling Method01:17

Systematic Sampling Method

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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Sampling from complex networks with high community structures.

Mostafa Salehi1, Hamid R Rabiee, Arezo Rajabi

  • 1Digital Media Lab, Department of Computer Engineering, AICTC Research Center, Sharif University of Technology, Tehran, Iran. mostafa_salehi@ce.sharif.edu

Chaos (Woodbury, N.Y.)
|July 5, 2012
PubMed
Summary
This summary is machine-generated.

We developed a new network sampling algorithm using PageRank concepts to explore communities. This method improves accuracy and community coverage compared to existing link-based techniques.

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Area of Science:

  • Network science
  • Data mining
  • Computer science

Background:

  • Network sampling is crucial for analyzing large-scale networks.
  • Existing methods struggle with networks exhibiting strong community structures.
  • PageRank algorithms offer insights into network topology and node importance.

Purpose of the Study:

  • To introduce a novel link-tracing sampling algorithm.
  • To enhance network sampling in community-rich networks.
  • To improve accuracy and community discovery in network analysis.

Main Methods:

  • A two-phase approach combining personalized PageRank approximation and community jumping.
  • Phase 1: Sampling nodes close to initial nodes via personalized PageRank.
  • Phase 2: Utilizing PageRank vectors and unknown neighbors for inter-community jumps.

Main Results:

  • The proposed algorithm demonstrates improved network sampling performance.
  • Empirical studies show higher accuracy compared to traditional link-based methods.
  • The algorithm successfully increases the number of visited communities.

Conclusions:

  • The novel link-tracing algorithm effectively samples networks with high community structures.
  • This method offers a significant advancement over existing network sampling techniques.
  • The approach is validated on both synthetic and real-world network data.