Selectivity-based spreading dynamics on complex networks

Most previous studies on spreading dynamics on complex networks are based on the assumption that a node can transmit infection to any of its neighbors with equal probability. In realistic situations, an infected node can preferentially select a targeted node and vice versa. We develop a first-order correction to the standard meanfield theory to address this type of more realistic spreading dynamics on complex networks. Our analysis reveals that, when small-degree nodes are selected more frequently as targets, infection can spread to a larger part of the network. However, when a small set of hub nodes dominates the dynamics, spreading can be severely suppressed. Our analysis yields more accurate predictions for the spreading dynamics than those from the standard mean-field approach.