Research map / Learning theory and algorithms / Submodular and game algorithms
Balancing information exposure in social networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Optimize the full balance objective instead of only the coverage term (ablation) | Cover · Twitter datasets, heterogeneous and correlated settings | remaining unbalanced vertices | Helped |
| Allow choices beyond common seeds, while requiring each step to match the best common-seed gain (ablation) | Common · Twitter datasets, heterogeneous setting | remaining unbalanced vertices | Helped |
| Choose seeds greedily to maximize the full balance objective rather than its coverage component (ablation) | Cover · Twitter datasets, heterogeneous setting | remaining unbalanced vertices | Helped |
| Choose seeds greedily to maximize the full balance objective rather than its coverage component (ablation) | Cover · Twitter datasets, correlated setting | remaining unbalanced vertices | Helped |
| Use Hedge rather than Greedy | Twitter datasets · US-elections | remaining unbalanced vertices | Hurt |
| Use Hedge rather than Greedy | Twitter datasets · Correlated setting | remaining unbalanced vertices | No clear effect |
| Use Hedge rather than BBLO | Twitter datasets · 12 cases at k = 20 | remaining unbalanced vertices | Mixed |
| Use Hedge rather than Union | Twitter datasets · k = 20 | remaining unbalanced vertices | Helped |
| Use Hedge rather than Intersection | Twitter datasets · k = 20 | remaining unbalanced vertices | Helped |
| Use Hedge rather than Random | Twitter datasets · k = 20 | remaining unbalanced vertices | Helped |
| Use Hedge rather than High-Degree | Twitter datasets · Heterogeneous setting | remaining unbalanced vertices: by a factor of two on average | Helped |
| Use Hedge rather than the two baselines | Twitter datasets · Correlated setting | remaining unbalanced vertices: by an order of magnitude | Helped |
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