Research map / Learning theory and algorithms / Fairness
Scalable Fair Influence Maximization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use welfare-fairness seed selection instead of community-aware highest-degree seeding | Email · Email, p=0.001 | EoF: 45.08% -> 51.91% | Helped |
| Use welfare-fairness seed selection instead of community-aware IMM seeding | Email · Email, p=0.005 | EoF: 23.63% -> 26.23% | Helped |
| Use welfare-fairness seed selection instead of community-aware highest-degree seeding | Email · Email, p=0.01 | PoF: 24.07% -> 2.57% | Helped |
| Increase the influence probability | FIMM · Email, p=0.001 to 0.01 | PoF: 21.77% -> 2.57% | Helped |
| Increase the inequality-aversion parameter | FIMM · Flixster, α=0.1 to 0.9 | EoF | Hurt |
| Use FIMM rather than IMM for seed selection on the pre-generated RR sets | Email · Email | running time (seconds): 0.011 -> 0.020 | Hurt |
| Use FIMM rather than IMM for seed selection on the pre-generated RR sets | FIMM · Amazon, Youtube, and DBLP | running time (seconds): Amazon: 0.028 -> 0.283; Youtube: 0.052 -> 3.592; DBLP: 0.063 -> 18.259 | Hurt |
| Use FIMM with a smaller seed budget | FIMM · Amazon, Youtube, and DBLP | EoF | Mixed |
Closest papers by meaning
- Fair Influence Maximization: a Welfare Optimization Approach (AAAI 2021 · similarity 0.90)
- Fairness in Social Influence Maximization via Optimal Transport (NeurIPS 2024 · similarity 0.89)
- Improving Fairness in Information Exposure by Adding Links (AAAI 2023 · similarity 0.89)
- On the Cost of Demographic Parity in Influence Maximization (AAAI 2023 · similarity 0.89)
- Hyperparametric Robust and Dynamic Influence Maximization (AAAI 2025 · similarity 0.85)
- REM: A Scalable Reinforced Multi-Expert Framework for Multiplex Influence Maximization (AAAI 2025 · similarity 0.83)
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