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Iterative embedding and reweighting of complex networks reveals community structure
Bianka Kovács1, Sadamori Kojaku2,3, Gergely Palla4,5
1Department of Biological Physics, Eötvös Loránd University, Budapest, Pázmány P. stny. 1/A, 1117, Hungary.
Scientific Reports
|July 26, 2024
Summary
This study introduces an iterative graph embedding and reweighting method to enhance community detection. The technique sharpens network clusters, improving the accuracy of identifying communities in complex networks.
Area of Science:
- Network Science
- Machine Learning
- Data Mining
Background:
- Graph embeddings represent network structures in low-dimensional spaces.
- Community structure is a key feature captured by graph embeddings.
Purpose of the Study:
- To develop a method that enhances the visibility and detectability of community structures in networks.
- To improve the performance of community detection algorithms using graph embeddings.
Main Methods:
- An iterative procedure involving graph embedding and link reweighting based on node proximity.
- Reinforcing intra-community links and weakening inter-community links.
Main Results:
- The iterative process significantly enhances geometric separation between communities.
- Simple link parsing can recover communities with high precision after the procedure.
- The method improves the performance of traditional community detection algorithms when used as a preprocessing step.
Conclusions:
- The proposed embedding and reweighting procedure effectively sharpens community structures in networks.
- This approach offers a robust pre-processing step for advanced community detection.
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