【摘 要】
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Optimizing the spread of influence in big-data online social networks is important for the design of efficient viral marketing strategies.As the viral spread of information on social network is a glob
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Optimizing the spread of influence in big-data online social networks is important for the design of efficient viral marketing strategies.As the viral spread of information on social network is a global process,it is commonly believed that measuring the influence of nodes and optimizating viral spreading would require the the whole network information.By mapping the spreading dynamics onto bond percolation in statistical physics,we find that for many stochastic spreading event,the information spreading happens in only one of the two well-separated phases: a locally confined phase with small number of influenced nodes,and a global viral spreading phase with a fixed fraction of whole network nodes that is invariant with respect to seed nodes and realizations.
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