Related Experiment Video
Updated: May 12, 2026
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
Robust detection of dynamic community structure in networks.
Danielle S Bassett1, Mason A Porter, Nicholas F Wymbs
1Department of Physics, University of California, Santa Barbara, California 93106, USA. dbassett@physics.ucsb.edu
Chaos (Woodbury, N.Y.)
|April 6, 2013
Summary
This study introduces robust methods for detecting community structure in time-dependent networks using statistical null models. These techniques improve the identification and validation of network modules, crucial for analyzing complex systems.
Area of Science:
- Network Science
- Statistical Physics
- Data Analysis
Background:
- Detecting community structure is vital for understanding complex systems.
- Time-dependent networks present unique challenges for community detection.
- Statistical null models are essential for validating network structures.
Purpose of the Study:
- To develop robust techniques for community detection in time-dependent networks.
- To utilize statistical null models for principled identification and validation of structural modules.
- To address challenges posed by noisy data and local optima in network analysis.
Main Methods:
- Employing statistical null models for community detection.
- Optimizing quality functions like modularity.
- Assessing statistical validity of identified community structures.
- Quantifying optimization and randomization variance.
- Developing a null model-corrected method for representative partitions.
Main Results:
- Demonstrated the role of null models in optimizing and validating community structure.
- Examined sensitivity to model parameters and identified system scales.
- Quantified optimization and randomization variance in network diagnostics.
- Developed a novel method to correct for statistical noise in partition sets.
- Applied methods to time-dependent networks from nonlinear oscillators and neuroscience data.
Conclusions:
- Statistical null models provide a principled framework for robust community detection in time-dependent networks.
- The developed methods enhance the reliability of identifying network modules.
- The approach is applicable to diverse complex systems, including neuroscience and physical systems.
Related Concept Videos
01:19Methods to Assess Microbial Communities
Microbial communities, comprising bacteria, archaea, and eukaryotic microorganisms, inhabit diverse ecosystems and play crucial roles in environmental and biological processes. Their diversity is defined by three main parameters: species richness (the number of distinct species), species abundance (the relative quantity of each species), and species evenness (how uniformly individual species are distributed in various locations). These factors together shape the structure and ecological balance...
01:14Stability of structures
In mechanical engineering, the stability of systems under various forces is critical for designing durable and efficient structures. One fundamental way to explore these concepts is by analyzing systems like two rods connected at a pivot point, O, with a torsional spring of spring constant k at the pivot point. This system is similar in appearance to a scissor jack used to change tires on a car. In this case, the arms of the linkage (equivalent to the rods in this system) are entirely vertical,...
02:26Protein Networks
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
02:26Protein Networks
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
01:02Quantifying 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...
01:20Structural Classification of Joints
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
A fibrous joint is where the adjacent bones are united by fibrous connective...