Fast and Simple Densest Subgraph with Predictions
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917408887799808 |
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| author | Bui, Thai Nguyen, Luan Vu, Hoa T. |
| author_facet | Bui, Thai Nguyen, Luan Vu, Hoa T. |
| contents | We study the densest subgraph problem and its NP-hard densest at-most-$k$ subgraph variant through the lens of learning-augmented algorithms. We show that, given a reasonably accurate predictor that estimates whether a node belongs to the solution (e.g., a machine learning classifier), one can design simple linear-time algorithms that achieve a $(1-ε)$approximation. Finally, we present experimental results demonstrating the effectiveness of our methods for the densest at-most-$k$ subgraph problem on real-world graphs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12600 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fast and Simple Densest Subgraph with Predictions Bui, Thai Nguyen, Luan Vu, Hoa T. Data Structures and Algorithms Machine Learning We study the densest subgraph problem and its NP-hard densest at-most-$k$ subgraph variant through the lens of learning-augmented algorithms. We show that, given a reasonably accurate predictor that estimates whether a node belongs to the solution (e.g., a machine learning classifier), one can design simple linear-time algorithms that achieve a $(1-ε)$approximation. Finally, we present experimental results demonstrating the effectiveness of our methods for the densest at-most-$k$ subgraph problem on real-world graphs. |
| title | Fast and Simple Densest Subgraph with Predictions |
| topic | Data Structures and Algorithms Machine Learning |
| url | https://arxiv.org/abs/2505.12600 |