Spreading dynamics in complex networks

Abstract

Searching for influential spreaders in complex networks is an issue of great\nsignificance for applications across various domains, ranging from the epidemic\ncontrol, innovation diffusion, viral marketing, social movement to idea\npropagation. In this paper, we first display some of the most important\ntheoretical models that describe spreading processes, and then discuss the\nproblem of locating both the individual and multiple influential spreaders\nrespectively. Recent approaches in these two topics are presented. For the\nidentification of privileged single spreaders, we summarize several widely used\ncentralities, such as degree, betweenness centrality, PageRank, k-shell, etc.\nWe investigate the empirical diffusion data in a large scale online social\ncommunity -- LiveJournal. With this extensive dataset, we find that various\nmeasures can convey very distinct information of nodes. Of all the users in\nLiveJournal social network, only a small fraction of them involve in spreading.\nFor the spreading processes in LiveJournal, while degree can locate nodes\nparticipating in information diffusion with higher probability, k-shell is more\neffective in finding nodes with large influence. Our results should provide\nuseful information for designing efficient spreading strategies in reality.\n