# DQ4FairIM: Fairness-aware Influence Maximization using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2512.00545 · HTML: https://mlsanity.com/paper/2512.00545/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 24 of its 68 references that are in the map.
- First posted on arXiv 29 Nov 2025.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 24 of 24)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Learning Combinatorial Optimization Algorithms over Graphs](https://mlsanity.com/paper/1704.01665/) (2017)
- [Discriminative Embeddings of Latent Variable Models for Structured Data](https://mlsanity.com/paper/1603.05629/) (2016)
- [CrossWalk: Fairness-enhanced Node Representation Learning](https://mlsanity.com/paper/2105.02725/) (2021)
- [Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency](https://mlsanity.com/paper/1404.0900/) (2014)
- [Group-Fairness in Influence Maximization](https://mlsanity.com/paper/1903.00967/) (2019)
- [Stop-and-Stare: Optimal Sampling Algorithms for Viral Marketing in Billion-scale Networks](https://mlsanity.com/paper/1605.07990/) (2016)
- [Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks](https://mlsanity.com/paper/2005.04074/) (2020)
- [A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization](https://mlsanity.com/paper/2211.03074/) (2022)
- [Survey on Fair Reinforcement Learning: Theory and Practice](https://mlsanity.com/paper/2205.10032/) (2022)
- [Contingency-Aware Influence Maximization: A Reinforcement Learning Approach](https://mlsanity.com/paper/2106.07039/) (2021)
- [Fair Influence Maximization: A Welfare Optimization Approach](https://mlsanity.com/paper/2006.07906/) (2020)
- [On the Fairness of Time-Critical Influence Maximization in Social Networks](https://mlsanity.com/paper/1905.06618/) (2019)
- [Gaps in Information Access in Social Networks](https://mlsanity.com/paper/1903.02047/) (2019)
- [FairSNA: Algorithmic Fairness in Social Network Analysis](https://mlsanity.com/paper/2209.01678/) (2022)
- [Centrality Measures in Complex Networks: A Survey](https://mlsanity.com/paper/2011.07190/) (2020)
- [IMRank: Influence Maximization via Finding Self-Consistent Ranking](https://mlsanity.com/paper/1402.3939/) (2014)
- [Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation Learning](https://mlsanity.com/paper/2402.03357/) (2024)
- [Fairness in Influence Maximization through Randomization](https://mlsanity.com/paper/2010.03438/) (2020)
- [A Unified Seeding Framework](https://mlsanity.com/paper/2011.08946/) (2020)
- [Homophily in Complex Networks: Measures, Models, and Applications](https://mlsanity.com/paper/2509.18289/) (2025)
- [Modeling Memetics using Edge Diversity](https://mlsanity.com/paper/1505.00457/) (2015)
- [Fair Influence Maximization in Social Networks: A Community-Based Evolutionary Algorithm](https://mlsanity.com/paper/2311.14288/) (2023)
- [Gender differences in online communication: A case study of Soccer](https://mlsanity.com/paper/2403.11051/) (2024)

## Built on by (most cited first, 1 of 1)

- [Fairness-Aware Network Embeddings: Methods, Applications, and Challenges](https://mlsanity.com/paper/2608.19381/) (2026)

## Abstract (arXiv)

The Influence Maximization (IM) problem aims to select a set of seed nodes within a given budget to maximize the spread of influence in a social network. However, real-world social networks have several structural inequalities, such as dominant majority groups and underrepresented minority groups. If these inequalities are not considered while designing IM algorithms, the outcomes might be biased, disproportionately benefiting majority groups while marginalizing minorities. In this work, we address this gap by designing a fairness-aware IM method using Reinforcement Learning (RL) that ensures equitable influence outreach across all communities, regardless of protected attributes. Fairness is incorporated using a maximin fairness objective, which prioritizes improving the outreach of the least-influenced group, pushing the solution toward an equitable influence distribution. We propose a novel fairness-aware deep RL method, called DQ4FairIM, that maximizes the expected number of influenced nodes by learning an RL policy. The learnt policy ensures that minority groups formulate the IM problem as a Markov Decision Process (MDP) and use deep Q-learning, combined with the Structure2Vec network embedding, earning together with Structure2Vec network embedding to solve the MDP. We perform extensive experiments on synthetic benchmarks and real-world networks to compare our method with fairness-agnostic and fairness-aware baselines. The results show that our method achieves a higher level of fairness while maintaining a better fairness-performance trade-off than baselines. Additionally, our approach learns effective seeding policies that generalize across problem instances without retraining, such as varying the network size or the number of seed nodes.