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Finding instabilities in the community structure of complex networks
David Gfeller1, Jean-Cédric Chappelier, Paolo De Los Rios
1Laboratoire de Biophysique Statistique, SB/ITP, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|December 31, 2005
Summary
This study introduces a novel method to find nodes between clusters in complex networks by adding noise to edge weights. This approach enhances cluster stability analysis and works with various weighted network clustering algorithms.
Area of Science:
- Complex network analysis
- Statistical physics
- Data science
Background:
- Traditional clustering algorithms often create non-overlapping partitions.
- Identifying nodes that bridge clusters is crucial for understanding network structure.
- Existing methods lack a general measure for cluster stability.
Purpose of the Study:
- To develop a method for identifying nodes situated between clusters in complex networks.
- To introduce a general measure for assessing the stability of identified clusters.
- To demonstrate the applicability of the method across different clustering algorithms.
Main Methods:
- Introducing noise to edge weights within complex networks.
- Utilizing a noise-adding technique to reveal inter-cluster nodes.
- Integrating the method with existing weighted network clustering algorithms.
Main Results:
- Successfully identified nodes located between distinct clusters.
- Developed a quantifiable measure for cluster stability.
- Demonstrated versatility by applying the method with two distinct clustering algorithms on real-world networks.
Conclusions:
- The proposed method effectively identifies inter-cluster nodes and quantifies cluster stability.
- The noise-adding technique offers a flexible approach compatible with various clustering algorithms.
- This advancement provides deeper insights into the structure and robustness of complex networks.