Robustness of community structure under edge addition.
1Division of Applied Mathematics, Brown University, Providence, Rhode Island 02912, USA.
Physical Review. E
|December 20, 2023
Summary
This study explores how adding connections affects network communities. Communities are more robust to random additions and stronger initial structures, with robustness varying by detection method.
Area of Science:
- Network science
- Complex systems analysis
- Data mining and community detection
Background:
- Communities are fundamental network structures.
- Understanding community robustness to perturbations is vital.
- Edge addition's impact on community robustness is understudied.
Purpose of the Study:
- Analyze the effect of edge addition on community robustness.
- Investigate random versus targeted edge addition scenarios.
- Evaluate robustness across different community detection algorithms.
Main Methods:
- Applied edge addition to synthetic and empirical temporal networks.
- Utilized Infomap, Label Propagation, Leiden, and Louvain algorithms.
- Assessed community structure changes using similarity metrics.
Main Results:
- Community robustness is higher with stronger initial partitions and random edge additions.
- Empirical data shows community similarity metrics influence robustness.
- Different community detection algorithms yield varying robustness levels.
Conclusions:
- Network community robustness is sensitive to edge addition strategies.
- The choice of community detection algorithm significantly impacts observed robustness.
- Further research is needed to understand edge addition effects in dynamic networks.
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