Scalable Influence Estimation in Continuous-Time Diffusion Networks Nan Du Le Song Manuel Gomez-Rodriguez

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.