Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks

Moein Khajehnejad, Ahmad Asgharian Rezaei, Mahmoudreza Babaei, Jessica Hoffmann, Mahdi Jalili, Adrian Weller

Abstract:

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.