# On the Fairness of Time-Critical Influence Maximization in Social Networks

arXiv: https://arxiv.org/abs/1905.06618 · HTML: https://mlsanity.com/paper/1905.06618/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2019.

## Built on by (most cited first, 10 of 10)

- [Fairness in Graph Mining: A Survey](https://mlsanity.com/paper/2204.09888/) (2022)
- [A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions](https://mlsanity.com/paper/2308.13821/) (2023)
- [On the Topology Awareness and Generalization Performance of Graph Neural Networks](https://mlsanity.com/paper/2403.04482/) (2024)
- [A Benchmark for Fairness-Aware Graph Learning](https://mlsanity.com/paper/2407.12112/) (2024)
- [Many-Objective Evolutionary Influence Maximization: Balancing Spread, Budget, Fairness, and Time](https://mlsanity.com/paper/2403.18755/) (2024)
- [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)
- [Temporal Scale Estimation for Oversampled Network Cascades: Theory, Algorithms, and Experiment](https://mlsanity.com/paper/2109.10937/) (2021)
- [Fair Information Spread on Social Networks with Community Structure](https://mlsanity.com/paper/2305.08791/) (2023)

## Abstract (arXiv)

Influence maximization has found applications in a wide range of real-world problems, for instance, viral marketing of products in an online social network, and information propagation of valuable information such as job vacancy advertisements and health-related information. While existing algorithmic techniques usually aim at maximizing the total number of people influenced, the population often comprises several socially salient groups, e.g., based on gender or race. As a result, these techniques could lead to disparity across different groups in receiving important information. Furthermore, in many of these applications, the spread of influence is time-critical, i.e., it is only beneficial to be influenced before a time deadline. As we show in this paper, the time-criticality of the information could further exacerbate the disparity of influence across groups. This disparity, introduced by algorithms aimed at maximizing total influence, could have far-reaching consequences, impacting people's prosperity and putting minority groups at a big disadvantage. In this work, we propose a notion of group fairness in time-critical influence maximization. We introduce surrogate objective functions to solve the influence maximization problem under fairness considerations. By exploiting the submodularity structure of our objectives, we provide computationally efficient algorithms with guarantees that are effective in enforcing fairness during the propagation process. We demonstrate the effectiveness of our approach through synthetic and real-world experiments.