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Related Experiment Video

Updated: Dec 28, 2025

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

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Community detection in networks without observing edges.

Till Hoffmann¹, Leto Peel², Renaud Lambiotte³

¹Department of Mathematics, Imperial College London, London SW7 2AZ, UK.

Science Advances

|February 12, 2020

Summary

This summary is machine-generated.

We introduce a Bayesian hierarchical model for time series community detection. This method propagates uncertainty, enabling multiscale analysis and optimal scale selection for financial and climate data.

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Area of Science:

Statistics
Data Science
Time Series Analysis

Background:

Community detection in time series is crucial for understanding complex systems.
Existing methods often rely on point estimates, neglecting uncertainty propagation.
Identifying communities across multiple scales requires robust analytical frameworks.

Purpose of the Study:

To develop a Bayesian hierarchical model for end-to-end time series community detection.
To incorporate uncertainty propagation from raw data to community labels.
To enable multiscale community detection and optimal scale selection.

Main Methods:

Bayesian hierarchical modeling
Uncertainty propagation
Model comparison for scale selection

Main Results:

An end-to-end community detection algorithm that preserves uncertainty.
Demonstrated capability for multiscale community detection.
Successful application to financial (S&P100 index) and climate data.

Conclusions:

The proposed Bayesian model offers a robust approach to time series community detection.
Uncertainty propagation enhances the reliability of community labels.
The method is versatile, applicable to diverse datasets like financial markets and climate patterns.