Searching for super spreaders of information

Searching for topological markers that can predict the spreading ability of nodes is crucial for plenty of applications, including propagation of social norms, viral marketing and diffusion of innovation. However, this problem has mainly been studied with modeling on specific systems, resulting in various model-dependent identifiers. Here we address the issue of locating privileged spreaders by following the real spreading dynamics in some of the most relevant networks, ranging from blogsphere (LiveJournal), microblogs (Twitter) and online social networks (Facebook) to information dissemination communities (scientific journals). Utilizing the complete network structure and records of diffusion of the published content, we find that k-shell is an effective predictor of influence, outperforming degree and PageRank. Furthermore, in the cases when the complete global structure is unavailable, we find that the sum of the nearest neighbors’ degrees is a reliable practical proxy for user’s influence. Our analysis sheds light on optimal designs of strategies of efficient information dissemination.

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