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Related Concept Videos

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
Distance Corrections01:15

Distance Corrections

To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
Distance Problem01:29

Distance Problem

When an object's velocity changes over time, the total distance traveled can be determined by summing small displacement intervals over short increments. This approach approximates the true distance through numerical summation and the use of integral calculus. An estimate of the total displacement can be obtained by measuring velocity at regular intervals and multiplying each value by the corresponding time step.If a runner accelerates over the first three seconds of a race, speed measurements...
Chromatographic Resolution01:15

Chromatographic Resolution

In chromatography, a solute moves through a chromatographic column and tends to spread, forming a Gaussian-shaped band. The longer the solute spends in the column, the broader the band becomes. The broadening can lead to overlaps within the column, affecting separation effectiveness.
The effectiveness of separation can be evaluated by determining the level of separation between two neighboring peaks in a chromatogram, which represents the individual components of a sample.
In chromatography,...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
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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Related Experiment Video

Updated: May 31, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Tolerating the community detection resolution limit with edge weighting.

Jonathan W Berry1, Bruce Hendrickson, Randall A LaViolette

  • 1Sandia National Laboratories, P.O. Box 5800, Albuquerque, New Mexico 87185, USA. jberry@sandia.gov

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|July 7, 2011
PubMed
Summary

Community detection algorithms struggle with small networks. This study introduces a modified algorithm that improves accuracy by adjusting edge weights, enabling better resolution of small communities in large networks.

Related Experiment Videos

Last Updated: May 31, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

  • Network Science
  • Data Mining
  • Computational Social Science

Background:

  • Modularity maximization algorithms are standard for community detection in large networks.
  • These algorithms face a resolution limit, failing to detect communities smaller than a certain size, impacting accuracy on real-world networks.
  • This limitation extends to weighted networks, where small communities may be missed.

Purpose of the Study:

  • To address the resolution limit in community detection algorithms for both unweighted and weighted networks.
  • To develop a modified algorithm capable of resolving smaller communities by adjusting edge weights.
  • To enhance the accuracy of community detection in complex networks.

Main Methods:

  • Extended Fortunato and Barthélemy's resolution limit argument to weighted networks.
  • Derived a condition for resolving small communities in weighted networks (requiring a low maximum intercommunity edge weight, ε).
  • Modified the Clauset, Newman, and Moore (CNM) algorithm to maximize weighted modularity and introduced a method for deriving new edge weights to achieve a low ε.

Main Results:

  • Demonstrated that weighted modularity algorithms can fail to resolve communities with less than √Wε/2 total edge weight.
  • The modified CNM algorithm showed greatly improved accuracy in detecting smaller communities.
  • The proposed CNM variant demonstrated competitive performance against state-of-the-art community detection methods on a standard benchmark.

Conclusions:

  • The resolution limit in community detection is a significant challenge for large networks.
  • Adjusting edge weights and modifying existing algorithms like CNM can overcome this limitation.
  • The developed weighted modularity maximization approach offers a promising solution for accurate community detection in complex networks.