Identifying localized influential spreaders of information spreading

Abstract

Identifying the influential spreaders of information spreading dynamics is a hot topic in the field of network science. To identify the influential spreaders, most previous studies were based on the global information of the network. In this paper, we propose a strategy for identifying the influential spreaders from a randomly selected initial-seed node. The seeds are connected as a chain, and are localized to the initial-seed. In our proposed preferentially random walk based influential spreaders identifying strategy, the walker’s movement is adjusted by neighbors’ degrees. The seeds are those nodes that the walker ever visited. Through extensive numerical simulations on artificial networks and four real-world networks, we find that selecting large degree nodes preferentially is more likely to find the most influential spreaders. The outbreak threshold decreases when preferentially select hubs. Our results shed some light into identifying the most localized influential spreaders.