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: 12944
Venue: VLDB
Year: 2022
Pagerank: 5.2094004e-05
Overall Rank: 9,848 | 32.44%
DOI: 10.14778/3551793.3551824

Incoming Non-self Citations Over Time

Authors

1. Ian P. Swift (University of Illinois Chicago)
2. Sana Ebrahimi (University of Illinois Chicago)
3. Azade Nova (Google)
4. Abolfazl Asudeh (University of Illinois Chicago)

BibTeX Citation

@article{swift_vldb22,
        title = {{Maximizing Fair Content Spread via Edge Suggestion in Social Networks}},
        author = {Swift, Ian P. and Ebrahimi, Sana and Nova, Azade and Asudeh, Abolfazl},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {11},
        pages = {2692--2705},
        doi = {10.14778/3551793.3551824},
        url = {https://doi.org/10.14778/3551793.3551824},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
5,489	Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching	2023	VLDB	6.200289e-05
10,238	Fair-Count-Min: Frequency Estimation under Equal Group-wise Approximation Factor	2026	SIGMOD	5.093636e-05
11,174	FairHash: A Fair and Memory/Time-efficient Hashmap	2024	SIGMOD	5.093636e-05
11,345	Host Profit Maximization: Leveraging Performance Incentives and User Flexibility	2024	VLDB	5.093636e-05

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.

Rank	Cited Paper	Year	Venue	Pagerank
863	Interventional Fairness : Causal Database Repair for Algorithmic Fairness	2019	SIGMOD	0.00013531835
1,340	Responsible Data Management	2020	VLDB	0.00011111667
1,597	Designing Fair Ranking Schemes	2019	SIGMOD	0.00010246472
4,658	OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning	2021	SIGMOD	6.5817368e-05
5,468	Ego-net Community Mining Applied to Friend Suggestion	2016	VLDB	6.2073549e-05
5,510	Responsible Data Integration: Next-generation Challenges	2022	SIGMOD	6.1919207e-05
5,631	Tailoring Data Source Distributions for Fairness-aware Data Integration	2021	VLDB	6.1427477e-05
6,197	SIGMOD 2020 Tutorial on Fairness and Bias in Peer Review and other Sociotechnical Intelligent Systems	2020	SIGMOD	5.946239e-05
6,637	Causal Feature Selection for Algorithmic Fairness	2022	SIGMOD	5.8170154e-05
6,709	Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification	2022	SIGMOD	5.7964916e-05
6,824	Fairly Evaluating and Scoring Items in a Data Set	2020	VLDB	5.7623529e-05
7,367	Identifying Insufficient Data Coverage for Ordinal Continuous-Valued Attributes	2021	SIGMOD	5.6319194e-05
7,449	Rank Aggregation with Proportionate Fairness	2022	SIGMOD	5.614576e-05

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