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Research map / Learning theory and algorithms / Submodular and game algorithms

Fair Influence Maximization: a Welfare Optimization Approach

AAAI 2021 · Learning theory and algorithms › Submodular and game algorithms · approach: Submodular influence maximization · 66 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Decrease inequality aversion parameter α (ablation)Isoelastic welfare · Sitka landslide risk managementutility gap: 4%-26%Helped
Decrease inequality aversion parameter α (ablation)Isoelastic welfare · Sitka landslide risk managementPoF: 36% - 5%Hurt
Use MMF instead of isoelastic welfareFair influence maximization · Sitka landslide risk managementPoF: surpassing 40%Hurt
Increase the influencer budget KFair influence maximization · Sitka landslide risk managementPoFHelped
Increase the influencer budget KIsoelastic welfare · Sitka landslide risk managementutility gapHurt
Increase the least-connected community's within-community edge probability q3 (ablation)Fair influence maximization · SBM relative connectednessutility gap: q_3 = 0.03Mixed
Increase the least-connected community's within-community edge probability q3 (ablation)Fair influence maximization · SBM relative connectednessPoF: q_3 = 0.03Mixed
Vary community connectedness rather than community size (ablation)Fair influence maximization · SBM relative connectedness and community sizePoF: up to 25%; less than 4%Hurt

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