Scalable Influence Estimation in Continuous-Time Diffusion Networks.

If a piece of information is released from a media site, can we predict whether it may spread to one million web pages, in a month ? This influence estimation problem is very challenging since both the time-sensitive nature of the task and the requirement of scalability need to be addressed simultaneously. In this paper, we propose a randomized algorithm for influence estimation in continuous-time diffusion networks. Our algorithm can estimate the influence of every node in a network with || nodes and || edges to an accuracy of using = (1/) randomizations and up to logarithmic factors (||+||) computations. When used as a subroutine in a greedy influence maximization approach, our proposed algorithm is guaranteed to find a set of nodes with the influence of at least (1 - 1/) OPT - 2 , where OPT is the optimal value. Experiments on both synthetic and real-world data show that the proposed algorithm can easily scale up to networks of millions of nodes while significantly improves over previous state-of-the-arts in terms of the accuracy of the estimated influence and the quality of the selected nodes in maximizing the influence.