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)
Incoming Non-self Citations Over Time
Authors
- 1. Ian P. Swift
- 2. Sana Ebrahimi
- 3. Azade Nova
- 4. Abolfazl Asudeh
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,018 | Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching | 2023 | VLDB | 6.5244015e-05 |
| 10,960 | FairHash: A Fair and Memory/Time-efficient Hashmap | 2024 | SIGMOD | 4.1945683e-05 |
| 11,139 | Host Profit Maximization: Leveraging Performance Incentives and User Flexibility | 2024 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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