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Efficient and effective algorithms for densest subgraph discovery and maintenance

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Abstract

The densest subgraph problem (DSP) is of great significance due to its wide applications in different domains. Meanwhile, diverse requirements in various applications lead to different density variants for DSP. Unfortunately, existing DSP algorithms cannot be easily extended to handle those variants efficiently and accurately. To fill this gap, we first unify different density metrics into a generalized density definition. We further propose a new model, c-core, to locate the general densest subgraph and show its advantage in accelerating the search process. Extensive experiments show that our c-core-based optimization can provide up to three orders of magnitude speedup over baselines. Methods for maintenance of c-core location are designed to accelerate updates on dynamic graphs. Moreover, we study an important variant of DSP under a size constraint, namely the densest-at-least-k-subgraph (DalkS) problem. We propose an algorithm based on graph decomposition, and it is likely to give a solution that is at least 0.8 of the optimal density in our experiments, while the state-of-the-art method can only ensure a solution with a density of at least 0.5 of the optimal density. Our experiments show that our DalkS algorithm can achieve at least 0.99 of the optimal density for over one-third of all possible size constraints. In addition, we develop an approximation algorithm for the DalkS problem that can be more efficient than the state-of-the-art algorithm while keeping the same approximation ratio of \(\frac{1}{3}\).

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Notes

  1. A solution with at least \(f \cdot OPT\) density (\(0 < f \le 1\)) means its density is at least f of the optimal density. For simplicity, we call this solution an \(f \cdot OPT\) solution.

  2. Empirically, the method based on [13] is slower than Greedy++ equipped with c-core location in our experiments.

  3. f-approximation method/algorithm means that for every input, the algorithm can guarantee a \(f \cdot OPT\) solution, \(0< f\le 1\). In general, all solutions output by the f-approximation algorithm are called f-approximation.

  4. We implement Algorithm 3 in our experiments. The algo has no significant influence on the time cost because the located core in Algorithm 2 is already very small.

  5. The process of adding a new vertex can be decomposed into the following: add an isoloated vertex; add its adjacent edges; increase its vertex weight.

  6. The coreness of a vertex is defined as the highest value of c for which the vertex is part of the corresponding c-core.

  7. This complexity is a version of Theorem 2.1 from [13] when their algo is equipped with our c-core location and density search strategy.

  8. Our code is available at https://github.com/Xyc-arch/Efficient-and-Effecive-algorithms-for-generalized-densest-subgraph-discovery

  9. We require \(0.909 \cdot OPT\) solution for Greedy++ because better solutions, e.g., \(0.99 \cdot OPT\) solution, cost too much time.

  10. Each vertical line symbolizes a distinct graph with overall graph size and core size compared.

  11. \(w_v = \frac{1}{2t}\) for v in left partition and \(w_v = \frac{t}{2}\) for v in right partition. We pick \(t = 2\) in our experiment.

  12. By combining techniques from Combinatorial-DalkSS it is a \(\text {max}(\frac{k}{|\tilde{K}^*|}, \frac{1}{2}) \cdot OPT\) solution.

  13. The reason we choose these datasets is that their sizes vary from small to large, and thus are representative.

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Acknowledgements

We would like to thank Wensheng Luo for his help during our revision. This work was partially supported by NSFC under Grant 62302421 and 62102341, Basic and Applied Basic Research Fund in Guangdong Province under Grant 2023A1515011280 and 2022A1515010166, and Shenzhen Science and Technology Program under Grants JCYJ20220530143602006 and ZDSYS 20211021111415025. Zhifeng Bao is supported in part by ARC DP220101434 and DP240101211. This paper was also supported by Guangdong Key Lab of Mathematical Foundations for Artificial Intelligence.

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  1. University of California, Berkeley, USA

    Yichen Xu

  2. The Chinese University of Hong Kong, Shenzhen, China

    Chenhao Ma & Yixiang Fang

  3. RMIT University, Melbourne, Australia

    Zhifeng Bao

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Xu, Y., Ma, C., Fang, Y. et al. Efficient and effective algorithms for densest subgraph discovery and maintenance. The VLDB Journal (2024). https://doi.org/10.1007/s00778-024-00855-y

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