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Community detection in networks by dynamical optimal transport formulation
Daniela Leite1, Diego Baptista2, Abdullahi A Ibrahim2
1Max Planck Institute for Intelligent Systems, Cyber Valley, 72076, Tübingen, Germany. daniela.leite@tuebingen.mpg.de.
This study introduces a novel Optimal Transport (OT) approach for network community detection. The method enhances community recovery accuracy by flexibly controlling information shared between node neighborhoods.
Area of Science:
- Network science
- Data mining
- Computational mathematics
Background:
- Community detection is crucial in network analysis.
- Existing methods, including Optimal Transport (OT) and Ollivier-Ricci curvature, compare node neighborhoods for classification.
- There's a need for more flexible OT-based approaches.
Purpose of the Study:
- To develop a tunable OT-based method for improved network community detection.
- To enhance control over information sharing between node neighborhoods.
- To increase the accuracy and flexibility of community recovery in diverse network structures.
Main Methods:
- An Optimal Transport (OT) based approach leveraging recent advances in OT theory.
- Tuning transportation regimes to control information flow between node neighborhoods.
- Comparison with existing OT-based community detection methods.
Main Results:
- The proposed model demonstrates flexible capture of various network structures.
- Achieved comparable or superior performance to other OT-based methods on synthetic networks.
- Identified communities that better represent node metadata in real-world networks.
Conclusions:
- The developed OT approach offers enhanced flexibility and accuracy in community detection.
- This method advances the understanding of geometric approaches for complex network pattern analysis.
- The findings suggest improved capabilities for capturing network structures and node relationships.
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