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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/2505.22684 |
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| _version_ | 1866915311328952320 |
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| author | Wang, Yufeng Bai, Yiguang Zhu, Tianqing Ayed, Ismail Ben Yuan, Jing |
| author_facet | Wang, Yufeng Bai, Yiguang Zhu, Tianqing Ayed, Ismail Ben Yuan, Jing |
| contents | Community partitioning is crucial in network analysis, with modularity optimization being the prevailing technique. However, traditional modularity-based methods often overlook fairness, a critical aspect in real-world applications. To address this, we introduce protected group networks and propose a novel fairness-modularity metric. This metric extends traditional modularity by explicitly incorporating fairness, and we prove that minimizing it yields naturally fair partitions for protected groups while maintaining theoretical soundness. We develop a general optimization framework for fairness partitioning and design the efficient Fair Fast Newman (FairFN) algorithm, enhancing the Fast Newman (FN) method to optimize both modularity and fairness. Experiments show FairFN achieves significantly improved fairness and high-quality partitions compared to state-of-the-art methods, especially on unbalanced datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_22684 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Recovering Fairness Directly from Modularity: a New Way for Fair Community Partitioning Wang, Yufeng Bai, Yiguang Zhu, Tianqing Ayed, Ismail Ben Yuan, Jing Social and Information Networks Machine Learning Community partitioning is crucial in network analysis, with modularity optimization being the prevailing technique. However, traditional modularity-based methods often overlook fairness, a critical aspect in real-world applications. To address this, we introduce protected group networks and propose a novel fairness-modularity metric. This metric extends traditional modularity by explicitly incorporating fairness, and we prove that minimizing it yields naturally fair partitions for protected groups while maintaining theoretical soundness. We develop a general optimization framework for fairness partitioning and design the efficient Fair Fast Newman (FairFN) algorithm, enhancing the Fast Newman (FN) method to optimize both modularity and fairness. Experiments show FairFN achieves significantly improved fairness and high-quality partitions compared to state-of-the-art methods, especially on unbalanced datasets. |
| title | Recovering Fairness Directly from Modularity: a New Way for Fair Community Partitioning |
| topic | Social and Information Networks Machine Learning |
| url | https://arxiv.org/abs/2505.22684 |