Research map / Learning theory and algorithms / Submodular and game algorithms
On Misinformation Containment in Online Social Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Sandwich, optimizing the objective and its upper and lower bounds, rather than only optimizing the objective | Max- overline-M problem · Higgs-10K, 3 cascades, budget 20 | M-active nodes: 180 to 100 | Helped |
| Optimize upper and lower bounds in addition to the objective (ablation) | Sandwich · Considered datasets | M-active nodes | Helped |
| Use the Random seed-selection baseline | Misinformation containment · Considered datasets | M-active nodes | No clear effect |
| Use HighWeight seed selection | Misinformation containment · Considered datasets | M-active nodes | No clear effect |
| Use Proximity seed selection | Misinformation containment · Considered datasets | M-active nodes | Mixed |
| Add positive seed nodes without accounting for cascade interactions | Max-overline-M objective · Illustrative network example | overline-M-active nodes: 5 to 4 | Hurt |
Closest papers by meaning
- Balancing Spreads of Influence in a Social Network (AAAI 2020 · similarity 0.87)
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- A Persuasive Approach to Combating Misinformation (ICML 2024 · similarity 0.83)
- MSR: A Multifaceted Self-Retrieval Framework for Microscopic Cascade Prediction (AAAI 2025 · similarity 0.82)
- Opinion Maximization in Social Networks by Modifying Internal Opinions (NeurIPS 2025 · similarity 0.82)
- Sample Complexity of Linear Regression Models for Opinion Formation in Networks (AAAI 2025 · similarity 0.81)
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