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A Benchmark Study of Deep-RL Methods for Maximum Coverage Problems over Graphs

Summary: Comprehensive benchmark of five Deep-RL heuristics for graph Maximum Coverage and Influence Maximization. Finds Lazy Greedy, IMM, and OPIM generally outperform them, exposing practical shortcomings and a narrow IM regime where Deep-RL prevails. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13761
Venue
VLDB
Year
2024
Pagerank
5.093636e-05
Overall Rank
11,278 | 22.63%
DOI
10.14778/3681954.3682029

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BibTeX Citation

@article{liang_vldb24,
        title = {{A Benchmark Study of Deep-RL Methods for Maximum Coverage Problems over Graphs}},
        author = {Liang, Zhicheng and Yang, Yu and Ke, Xiangyu and Xiao, Xiaokui and Gao, Yunjun},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {11},
        pages = {3666--3679},
        doi = {10.14778/3681954.3682029},
        url = {https://doi.org/10.14778/3681954.3682029},
        year = {2024}
}

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