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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.13272 |
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| _version_ | 1866913846891905024 |
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| author | Avellone, Alessandro Bartesaghi, Paolo Benati, Stefano Charalambous, Christos Grassi, Rosanna |
| author_facet | Avellone, Alessandro Bartesaghi, Paolo Benati, Stefano Charalambous, Christos Grassi, Rosanna |
| contents | Modularity and persistence probability are two widely used quality functions for detecting communities in complex networks. In this paper, we introduce a new objective function called null-adjusted persistence, which incorporates features from both modularity and persistence probability, as it implies a comparison of persistence probability with the same null model of modularity. We prove key analytic properties of this new function. We show that the null-adjusted persistence overcomes the limitations of modularity, such as scaling behavior and resolution limits, and the limitation of the persistence probability, which is an increasing function with respect to the cluster size. We propose to find the partition that maximizes the null-adjusted persistence with a variation of the Louvain method and we tested its effectiveness on benchmark and real networks. We found out that maximizing null-adjusted persistence outperforms modularity maximization, as it detects higher resolution partitions in dense and large networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13272 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Null-adjusted persistence function for high-resolution community detection Avellone, Alessandro Bartesaghi, Paolo Benati, Stefano Charalambous, Christos Grassi, Rosanna Physics and Society Optimization and Control Modularity and persistence probability are two widely used quality functions for detecting communities in complex networks. In this paper, we introduce a new objective function called null-adjusted persistence, which incorporates features from both modularity and persistence probability, as it implies a comparison of persistence probability with the same null model of modularity. We prove key analytic properties of this new function. We show that the null-adjusted persistence overcomes the limitations of modularity, such as scaling behavior and resolution limits, and the limitation of the persistence probability, which is an increasing function with respect to the cluster size. We propose to find the partition that maximizes the null-adjusted persistence with a variation of the Louvain method and we tested its effectiveness on benchmark and real networks. We found out that maximizing null-adjusted persistence outperforms modularity maximization, as it detects higher resolution partitions in dense and large networks. |
| title | Null-adjusted persistence function for high-resolution community detection |
| topic | Physics and Society Optimization and Control |
| url | https://arxiv.org/abs/2505.13272 |