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Consistency landscape of network communities.
Daekyung Lee1, Sang Hoon Lee2,3, Beom Jun Kim1
1Department of Physics, Sungkyunkwan University, Suwon 16419, Korea.
Physical Review. E
|June 17, 2021
Summary
Determining network community resolution is challenging. This study introduces a novel framework using community structure consistency to reliably quantify community detection validity across various resolutions.
Area of Science:
- Network Science
- Data Analysis
- Computational Social Science
Background:
- Community detection is crucial for analyzing network mesoscale structures.
- Selecting an appropriate community resolution remains a significant practical challenge due to the lack of standardized criteria.
- Previous methods often rely on ad hoc heuristics for resolution determination.
Purpose of the Study:
- To address the challenge of assigning a reasonable community resolution in network analysis.
- To introduce a novel framework for quantifying the reliability of detected communities based on consistency.
- To provide a community resolution landscape for networks.
Main Methods:
- Harnessing the concept of consistency in network community structures.
- Exploiting ambiguity in stochastic detection algorithms.
- Quantifying community reliability using global and local inconsistency measures across multiple detection processes.
Main Results:
- The proposed framework effectively displays the community resolution landscape of networks.
- It quantifies the reliability of detected communities for a given resolution parameter.
- Demonstrated effectiveness on both synthetic and real-world network data.
Conclusions:
- The developed method offers a reliable approach to determining community resolution.
- It enhances the practical application of community detection algorithms.
- Provides fundamental insights into network community structures and their stability.
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