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)
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Authors
- 1. Tianyou Gao
- 2. Takayuki Ito
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 180 | Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency | 2014 | SIGMOD | 0.00037135181 |
| 337 | Influence Maximization in Near-Linear Time: A Martingale Approach | 2015 | SIGMOD | 0.00027011645 |
| 436 | Stop-and-Stare: Optimal Sampling Algorithms for Viral Marketing in Billion-scale Networks | 2016 | SIGMOD | 0.00023259324 |
| 1,801 | Online Processing Algorithms for Influence Maximization | 2018 | SIGMOD | 0.00010510943 |
| 2,371 | From Competition to Complementarity: Comparative Influence Diffusion and Maximization | 2016 | VLDB | 8.9482922e-05 |
| 2,664 | Influence Maximization Revisited: Efficient Reverse Reachable Set Generation with Bound Tightened | 2020 | SIGMOD | 8.3512717e-05 |
| 6,179 | Efficient Approximation Algorithms for Adaptive Seed Minimization | 2019 | SIGMOD | 5.1691599e-05 |
| 8,741 | Efficient Approximation Algorithms for Minimum Cost Seed Selection with Probabilistic Coverage Guarantee | 2024 | SIGMOD | 4.456315e-05 |
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