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arXiv · 2505.12600

Fast and Simple Densest Subgraph with Predictions

Thai Bui·Luan Nguyen·Hoa T. Vu
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Abstract

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 $(1-\epsilon)$approximation. Finally, we present experimental results demonstrating the effectiveness of our methods for the densest at-most-$k$ subgraph problem on real-world graphs.

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Thai Bui, Luan Nguyen, Hoa T. Vu. 2025-05-19. Fast and Simple Densest Subgraph with Predictions. https://arxiv.org/abs/2505.12600

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