Local structure can identify and quantify influential global spreaders in large scale social networks.

ABSTRACT: Measuring and optimizing the influence of nodes in big-data online social networks are important for many practical applications, such as the viral marketing and the adoption of new products. As the viral spreading on a social network is a global process, it is commonly believed that measuring the influence of nodes inevitably requires the knowledge of the entire network. Using percolation theory, we show that the spreading process displays a nucleation behavior: Once a piece of information spreads from the seeds to more than a small characteristic number of nodes, it reaches a point of no return and will quickly reach the percolation cluster, regardless of the entire network structure; otherwise the spreading will be contained locally. Thus, we find that, without the knowledge of the enti

SUBMITTER: Hu Y 

PROVIDER: S-EPMC6055149 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature