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Maximizing Fair Content Spread via Edge Suggestion in Social Networks

Summary: Fairness wrapper for edge suggestions to maximize content spread with equitable reach. NP-hard and inapproximable unless P=NP; uses LP-relaxation with randomized rounding for fixed fairness/spread, plus a scalable iterative-sampling method achieving near-zero unfairness and 43% lift on up to 0.5M nodes. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12756
Venue
VLDB
Year
2022
Pagerank
4.299267e-05
Overall Rank
9,712 | 32.44%
DOI
10.14778/3551793.3551824

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