Being an influencer is hard: The complexity of influence maximization in temporal graphs with a fixed source

Abstract

We consider the influence maximization problem over a temporal graph. We deviate from the standard model of influence maximization, where the goal is to choose the most influential vertices. In our model, we are given a fixed vertex and the goal is to find the best time steps to transmit so that the influence of this vertex is maximized. We frame this problem as a spreading process that follows a variant of the susceptible-infected-susceptible (SIS) model and focus on four objective functions. In the MaxSpread objective, the goal is to maximize the number of vertices that get infected at least once. In MaxViral and MaxViralTstep, the goal is to maximize the number of vertices that are infected at the same time step and at a given time step, respectively. Finally, in MinNonViralTime, the goal is to maximize the number of vertices that are infected in every d time-step window.