Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks
This addresses fairness issues in influence maximization for applications such as viral marketing and vaccinations, representing a novel application of embeddings but with incremental methodological contributions.
The paper tackles fair influence maximization in social networks by introducing Adversarial Graph Embeddings to reduce disparities in influence across sensitive attributes like race or gender, achieving dramatic disparity reduction while remaining competitive with state-of-the-art methods.
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.