  In this paper, we study the problem of robust influence maximization in the
independent cascade model under a hyperparametric assumption. In social
networks users influence and are influenced by individuals with similar
characteristics and as such, they are associated with some features. A recent
surging research direction in influence maximization focuses on the case where
the edge probabilities on the graph are not arbitrary but are generated as a
function of the features of the users and a global hyperparameter. We propose a
model where the objective is to maximize the worst-case number of influenced
users for any possible value of that hyperparameter. We provide theoretical
results showing that proper robust solution in our model is NP-hard and an
algorithm that achieves improper robust optimization. We make-use of sampling
based techniques and of the renowned multiplicative weight updates algorithm.
Additionally, we validate our method empirically and prove that it outperforms
the state-of-the-art robust influence maximization techniques.
