Research map / Learning theory and algorithms / Submodular and game algorithms
Balancing Spreads of Influence in a Social Network
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Combine separate algorithms for zero and positive initial exposure | Heterogeneous BAL(μ,ν) · Heterogeneous BAL(μ,ν) | approximation ratio: (1-\frac{1}{e}-\eps)^{\frac{\nu}{2}}(\frac{1}{2|V|})^{\frac{\nu-2}{2}}\nu^{-\frac{2 u - 3}{2}} | Helped |
| Use a fictitious campaign to select seeds and propagate them in the same ν campaigns | Correlated BAL(μ,ν) · Correlated BAL(μ,ν) | approximation ratio: \frac{1-1/e-\eps}{2(\nu + 1)} | Helped |
| Generalize the balancing objective to require exposure to at least three campaigns | Heterogeneous BAL(μ,ν) · Heterogeneous BAL(μ,ν), ν≥3 | approximation hardness: no \(n^{-g(n)}\)-approximation algorithm with \(g(n)=o(1)\) | Hurt |
| Raise the campaign threshold to at least four | Heterogeneous BAL(μ,ν) · Heterogeneous BAL(μ,ν), ν≥4 | approximation hardness: no \(n^{-\epsilon}\)-approximation algorithm | Hurt |
Closest papers by meaning
- Balancing information exposure in social networks (NeurIPS 2017 · similarity 0.92)
- Better Bounds on the Adaptivity Gap of Influence Maximization under Full-adoption Feedback (AAAI 2021 · similarity 0.87)
- On Misinformation Containment in Online Social Networks (NeurIPS 2018 · similarity 0.87)
- Motif-oriented influence maximization for viral marketing in large-scale social networks (NeurIPS 2024 · similarity 0.85)
- Fairness in Social Influence Maximization via Optimal Transport (NeurIPS 2024 · similarity 0.85)
- Sample Complexity of Linear Regression Models for Opinion Formation in Networks (AAAI 2025 · similarity 0.85)
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