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Related Concept Videos

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...
Random Variables01:09

Random Variables

A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
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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...
Attribution Theory00:56

Attribution Theory

Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958). An internal factor is an...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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Community-based interventions in mental health represent a paradigm shift from institution-centered care to treatments embedded within the fabric of local communities. By prioritizing inclusion and leveraging existing societal structures, this approach fosters a supportive environment conducive to addressing mental health challenges while promoting individual dignity and agency.
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Related Experiment Video

Updated: Jun 16, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Community-aware biased random walks for community detection in attribute networks.

Jin Zhang1, Hailu Yang2, Jun Li1

  • 1College of Intelligent Engineering, Harbin Institute of Petroleum, Harbin, 150028, Heilongjiang, China.

Scientific Reports
|June 14, 2026
PubMed
Summary

This study introduces a novel community detection method using community-aware biased random walks in attributed networks. It improves upon existing techniques by considering node attributes and adaptively setting random walk parameters for better community identification.

Keywords:
Attributed networkCommunity detectionNetwork embeddingRandom walk

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Area of Science:

  • Network analysis
  • Data mining
  • Machine learning

Background:

  • Community detection is crucial for understanding network structures.
  • Existing network embedding methods often ignore node attributes and require manual parameter tuning.
  • Random walk-based methods face challenges with node attributes and parameter settings.

Purpose of the Study:

  • To propose a community-aware biased random walk method for community detection in attributed networks.
  • To address limitations of existing methods by incorporating node attributes and adaptive parameter settings.
  • To improve the accuracy and efficiency of community detection.

Main Methods:

  • A novel network embedding method based on community-aware biased random walk.
  • Jointly leveraging topological and attribute similarity to guide random walks.
  • Adaptive setting of random walk parameters based on node degrees and attributes.
  • Utilizing the skip-gram model for learning node embeddings.
  • Employing clustering algorithms for final community detection.

Main Results:

  • The proposed method effectively identifies communities in attributed networks.
  • It outperforms existing methods by considering both topological and attribute information.
  • Adaptive parameter setting prevents oversampling of low-degree or low-attribute nodes.
  • Experimental validation on real-world and synthetic datasets confirms effectiveness.

Conclusions:

  • The community-aware biased random walk method offers a robust approach to community detection in attributed networks.
  • Integrating node attributes and adaptive random walks enhances community identification accuracy.
  • This method provides a more efficient and effective solution compared to traditional techniques.