Research map / Learning theory and algorithms / Fairness
On the Cost of Demographic Parity in Influence Maximization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Relax perfect demographic parity to allow violation x/16 (ablation) | grdy_grp+lp on synthetic networks · Synthetic networks (gender and region communities) | coverage relative to grdy_im: 55% -> 81% | Helped |
| Relax perfect demographic parity to allow violation x/16 (ablation) | maxmin+lp on synthetic networks · Synthetic networks (gender and region communities) | coverage relative to grdy_im: 60% -> 85% | Helped |
| Relax perfect demographic parity to allow violation x/4 (ablation) | grdy_grp+lp on random singleton communities · Random networks (n=200, k=25) | coverage relative to grdy_im: 0 -> 75% | Helped |
| Relax perfect demographic parity to allow violation x/4 (ablation) | maxmin+lp on random singleton communities · Random networks (n=200, k=25) | coverage relative to grdy_im: 0 -> 67% | Helped |
| Increase tolerated fairness violation from x/16 to x/8 (ablation) | grdy_grp+lp on Facebook · Facebook (BFS communities, m=2, k=50) | coverage relative to grdy_im: 55% -> 99% | Helped |
| Increase tolerated fairness violation from x/16 to x/8 (ablation) | grdy_grp+lp on Facebook · Facebook (BFS communities, m=2, k=50) | fairness violation relative to grdy_im: 7% -> 23% | Hurt |
| Require exact parity rather than allow a fairness violation (ablation) | grdy_grp+lp, maxmin+lp, and ind_lp on random singleton communities · Random networks (n=200, k=25) | coverage | Hurt |
| Use ind_lp instead of grdy_grp+lp or maxmin+lp | Influence maximization under demographic parity · Random and synthetic networks | coverage | Hurt |
Closest papers by meaning
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- Fairness without Harm: An Influence-Guided Active Sampling Approach (NeurIPS 2024 · similarity 0.85)
- Geometry of Relaxed Fair Regression: A Unified Framework for Aware and Unaware Settings (NeurIPS 2026 · similarity 0.85)
- Unveiling Environmental Sensitivity of Individual Gains in Influence Maximization (NeurIPS 2025 · similarity 0.85)
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