# Group-Fairness in Influence Maximization

arXiv: https://arxiv.org/abs/1903.00967 · HTML: https://mlsanity.com/paper/1903.00967/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2019.

## Built on by (most cited first, 21 of 21)

- [Fairness in Graph Mining: A Survey](https://mlsanity.com/paper/2204.09888/) (2022)
- [Fairness Amidst Non-IID Graph Data: A Literature Review](https://mlsanity.com/paper/2202.07170/) (2022)
- [A Survey on Fairness for Machine Learning on Graphs](https://mlsanity.com/paper/2205.05396/) (2022)
- [Fairness Maximization among Offline Agents in Online-Matching Markets](https://mlsanity.com/paper/2109.08934/) (2021)
- [Deceptive Fairness Attacks on Graphs via Meta Learning](https://mlsanity.com/paper/2310.15653/) (2023)
- [Group Equality in Adaptive Submodular Maximization](https://mlsanity.com/paper/2207.03364/) (2022)
- [Group Fairness in Non-monotone Submodular Maximization](https://mlsanity.com/paper/2302.01546/) (2023)
- [Fairness in Streaming Submodular Maximization over a Matroid Constraint](https://mlsanity.com/paper/2305.15118/) (2023)
- [Balancing Utility and Fairness in Submodular Maximization (Technical Report)](https://mlsanity.com/paper/2211.00980/) (2022)
- [Achieving Long-term Fairness in Submodular Maximization through Randomization](https://mlsanity.com/paper/2304.04700/) (2023)
- [Beyond Submodularity: A Unified Framework of Randomized Set Selection with Group Fairness Constraints](https://mlsanity.com/paper/2304.06596/) (2023)
- [Many-Objective Evolutionary Influence Maximization: Balancing Spread, Budget, Fairness, and Time](https://mlsanity.com/paper/2403.18755/) (2024)
- [Proportional Selection in Networks](https://mlsanity.com/paper/2502.03545/) (2025)
- [Optimizing Health Coverage in Ethiopia: A Learning-augmented Approach and Persistent Proportionality Under an Online Budget](https://mlsanity.com/paper/2509.00135/) (2025)
- [DQ4FairIM: Fairness-aware Influence Maximization using Deep Reinforcement Learning](https://mlsanity.com/paper/2512.00545/) (2025)
- [Promoting Fairness in Information Access within Social Networks](https://mlsanity.com/paper/2512.14711/) (2025)
- [Efficient Edge Rewiring Strategies for Enhancing PageRank Fairness](https://mlsanity.com/paper/2602.02512/) (2026)
- [Fair Information Spread on Social Networks with Community Structure](https://mlsanity.com/paper/2305.08791/) (2023)
- [Fair Minimum Labeling: Efficient Temporal Network Activations for Reachability and Equity](https://mlsanity.com/paper/2510.03899/) (2025)
- [Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategies](https://mlsanity.com/paper/2601.07884/) (2026)
- [Price of Fairness in Short-Term and Long-Term Algorithmic Selections](https://mlsanity.com/paper/2605.06227/) (2026)

## Abstract (arXiv)

Influence maximization is a widely used model for information dissemination in social networks. Recent work has employed such interventions across a wide range of social problems, spanning public health, substance abuse, and international development (to name a few examples). A critical but understudied question is whether the benefits of such interventions are fairly distributed across different groups in the population; e.g., avoiding discrimination with respect to sensitive attributes such as race or gender. Drawing on legal and game-theoretic concepts, we introduce formal definitions of fairness in influence maximization. We provide an algorithmic framework to find solutions which satisfy fairness constraints, and in the process improve the state of the art for general multi-objective submodular maximization problems. Experimental results on real data from an HIV prevention intervention for homeless youth show that standard influence maximization techniques oftentimes neglect smaller groups which contribute less to overall utility, resulting in a disparity which our proposed algorithms substantially reduce.