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Robust Fair Influence Maximization under Multiple Community Partitions

Summary: Robust FIM over multiple candidate community partitions/fairness settings, optimizing worst-case normalized fairness objective rather than a single given partition. Hardness is near-unconditional; bicriteria approximation via submodular saturation, with S-HIST/SG-HIST instantiations achieving robust near-(1-1/e) performance. (summarized by gpt-5-mini on Apr 11 2026)

Paper ID
7709
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,495 | 28.00%
DOI
10.1145/3786692

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BibTeX Citation

@inproceedings{gao_sigmod26,
        title = {{Robust Fair Influence Maximization under Multiple Community Partitions}},
        author = {Gao, Tianyou and Ito, Takayuki},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3786692},
        url = {https://dl.acm.org/doi/10.1145/3786692},
        year = {2026}
}

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