  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.
