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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. Highlights • Proposing a strategy for identifying the localized influential spreaders from a randomly selected initial-seed node. • Selecting large degree nodes preferentially is more likely to find the most influential spreaders. • The effectiveness of the strategy is verified in both artificial and real-world networks. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we study the effects of heterogeneous population on the dynamics of social contagions on multiplex networks. We assume a fraction of f nodes with a higher adoption threshold T > 1 , and the remaining fraction of 1 − f nodes with adoption threshold 1. A social contagion model is proposed to describe the social contagions, in which a susceptible node adopting the contagion only when its received accumulated information is larger than the adoption threshold in either subnetwork. With an edge-based compartmental approach and extensive numerical simulations, we find that the system exhibits a continuous phase transition for small values of f , while shows a hybrid phase transition for relatively large values of f and T. For homogeneous multiplex networks the hybrid phase transition occurs, while there is only a continuous phase transition for heterogeneous multiplex networks. Our theoretical predictions agree well with numerical simulations. Highlights • Proposing a social contagion model on multiplex networks with heterogeneous population. • An edge-based compartmental approach is developed, and well predicts numerical simulations. • The system exhibits a continuous or hybrid phase transition. [ABSTRACT FROM AUTHOR]