Robust Influence Maximization for Hyperparametric Models

TL;DR

This paper addresses robust influence maximization in social networks with feature-based influence probabilities, proposing algorithms that handle hyperparameter uncertainty and outperform existing methods.

Contribution

It introduces a hyperparametric model for influence maximization, proves NP-hardness of the robust problem, and develops an effective approximation algorithm using sampling and multiplicative weights.

Findings

The proposed method outperforms state-of-the-art techniques in empirical tests.

The problem of robust influence maximization under hyperparametric models is NP-hard.

An improper robust optimization algorithm is developed using sampling and multiplicative weights.

Abstract

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.