On Multiplexity-Aware Influence Spread in Social Networks

The analysis of influence spread in monoplex (single-layer) networks where all interactions are treated equally has been widely studied since the early 2000s. In complex networks, e.g., social networks, multi-agent networks, etc., the elements of networks interact in many ways, which results in multiplex (multiple-layer) networks. In this paper, we analyze the multiplexity-aware influence spread in two extended linear threshold models with two different protocols that explain how an agent updates its state combining signals from all layers. We then generalize the cohesiveness structural property to multiplex networks that explains why multiplexity facilitates or impedes a propagation. Based on this property, we present the optimal solutions by solving integer linear programming problems for influence minimization in multiplex linear threshold models.