Improving Fairness in Information Exposure by Adding Links

Fairness in influence maximization has been a very active research topic recently. Most works this context study the question of how to find seeding strategies (deterministic or probabilistic) such that nodes communities network get their fair share coverage. Different fairness criteria have used context. All these assume entity is spreading information an inherent interest fairly, otherwise why would they want use developed algorithms? This assumption may however be flawed reality -- purely efficiency-oriented. In paper we propose two optimization problems with goal modify structure by adding links way efficiency-oriented becomes automatically fair. We proposed both from theoretical and experimental perspective, is, give several hardness approximation results, provide efficient algorithms for some special cases, more importantly heuristics solving one practice. our then first compare against each other establish most successful one. second experiment, show approach can That already after few edges networks greedy algorithm maximizes spread surpasses all fairness-tailored terms ex-post fairness. Maybe surprisingly, even achieves values are comparable better than ex-ante currently optimize