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Assessing the consistency of community structure in complex networks
Matthew Steen1, Satoru Hayasaka, Karen Joyce
1Department of Radiology, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, USA.
Summary
This study introduces scaled inclusivity to measure how community structures change across networks. This new method tracks individual node consistency, offering a novel approach for network analysis.
Area of Science:
- Network science
- Computational social science
Background:
- Community structure is crucial in complex network analysis.
- Existing methods often focus on single network evolution or community-level changes, neglecting individual node dynamics.
- Analyzing changing community structures across multiple networks requires robust methodologies.
Purpose of the Study:
- To introduce a novel method, scaled inclusivity, for quantifying changes in community structure across networks.
- To evaluate the consistency of individual node classifications independently.
- To provide a method applicable to both cross-sectional and longitudinal network data.
Main Methods:
- Developed and applied the scaled inclusivity metric.
- Evaluated node classification consistency across different networks.
- Utilized simulated networks of US cities and real-world networks of American college football teams.
Main Results:
- Scaled inclusivity successfully quantified changes in community structure.
- The method provided reasonable results for individual node consistency in both simulated and real networks.
- Demonstrated the applicability of scaled inclusivity across diverse network types.
Conclusions:
- Scaled inclusivity offers a valuable tool for measuring community structure evolution in complex networks.
- The method's focus on individual node consistency provides a granular perspective on network changes.
- This approach enhances the analysis of dynamic network structures over time and across datasets.
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