# Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks

arXiv: https://arxiv.org/abs/2005.04074 · HTML: https://mlsanity.com/paper/2005.04074/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2020.

## Built on by (most cited first, 15 of 15)

- [Fairness in Graph Mining: A Survey](https://mlsanity.com/paper/2204.09888/) (2022)
- [CrossWalk: Fairness-enhanced Node Representation Learning](https://mlsanity.com/paper/2105.02725/) (2021)
- [Fairness for Image Generation with Uncertain Sensitive Attributes](https://mlsanity.com/paper/2106.12182/) (2021)
- [Fairness-Aware Graph Neural Networks: A Survey](https://mlsanity.com/paper/2307.03929/) (2023)
- [A Survey on Fairness for Machine Learning on Graphs](https://mlsanity.com/paper/2205.05396/) (2022)
- [A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization](https://mlsanity.com/paper/2211.03074/) (2022)
- [Graph Learning with Localized Neighborhood Fairness](https://mlsanity.com/paper/2212.12040/) (2022)
- [Endowing Pre-trained Graph Models with Provable Fairness](https://mlsanity.com/paper/2402.12161/) (2024)
- [A Benchmark for Fairness-Aware Graph Learning](https://mlsanity.com/paper/2407.12112/) (2024)
- [Vital Node Identification in Complex Networks Using a Machine Learning-Based Approach](https://mlsanity.com/paper/2202.06229/) (2022)
- [CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on Graphs](https://mlsanity.com/paper/2304.04391/) (2023)
- [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)
- [Learning Asymmetric Embedding for Attributed Networks via Convolutional Neural Network](https://mlsanity.com/paper/2202.06307/) (2022)
- [Fairness-Aware Network Embeddings: Methods, Applications, and Challenges](https://mlsanity.com/paper/2608.19381/) (2026)

## Abstract (arXiv)

Influence maximization is a widely studied topic in network science, where the aim is to reach the maximum possible number of nodes, while only targeting a small initial set of individuals. It has critical applications in many fields, including viral marketing, information propagation, news dissemination, and vaccinations. However, the objective does not usually take into account whether the final set of influenced nodes is fair with respect to sensitive attributes, such as race or gender. Here we address fair influence maximization, aiming to reach minorities more equitably. We introduce Adversarial Graph Embeddings: we co-train an auto-encoder for graph embedding and a discriminator to discern sensitive attributes. This leads to embeddings which are similarly distributed across sensitive attributes. We then find a good initial set by clustering the embeddings. We believe we are the first to use embeddings for the task of fair influence maximization. While there are typically trade-offs between fairness and influence maximization objectives, our experiments on synthetic and real-world datasets show that our approach dramatically reduces disparity while remaining competitive with state-of-the-art influence maximization methods.