Research map / Learning theory and algorithms / Fairness
Improving Fairness in Information Exposure by Adding Links
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use to_minC_infl instead of grdy_al for selecting added links | FIMALG heuristics · Random and synthetic instances | minimum community coverage | Helped |
| Use to_minC_infl instead of to_minC_min for selecting added links | FIMALG heuristics · Arenas, ca-GrQc, and email-Eu-core | minimum community coverage | Helped |
| Add links selected by to_minC_infl before running standard greedy influence maximization | Standard greedy influence maximization · Random and synthetic instances | ex-post minimum community coverage | Helped |
| Add 10 links selected by to_minC_infl | Standard greedy influence maximization · Real-world instances | ex-post minimum community coverage | Helped |
| Add 50 links selected by to_minC_infl | Standard greedy influence maximization · Real-world instances | minimum community coverage | Helped |
Closest papers by meaning
- Fairness in Social Influence Maximization via Optimal Transport (NeurIPS 2024 · similarity 0.92)
- Scalable Fair Influence Maximization (NeurIPS 2023 · similarity 0.89)
- On the Cost of Demographic Parity in Influence Maximization (AAAI 2023 · similarity 0.89)
- Fair Influence Maximization: a Welfare Optimization Approach (AAAI 2021 · similarity 0.87)
- Balancing information exposure in social networks (NeurIPS 2017 · similarity 0.86)
- Fair Sampling in Diffusion Models through Switching Mechanism (AAAI 2024 · similarity 0.82)
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