If a piece of information is released from a media site, can it spread, in 1 month, to a million web pages? This influence estimation problem is very challenging since both the time-sensitive nature of the problem and the issue 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 |V| nodes and |E| edges to an accuracy of $\varepsilon$ using $n=O(1/\varepsilon^2)$ randomizations and up to logarithmic factors O(n|E|+n|V|) computations. When used as a subroutine in a greedy influence maximization algorithm, our proposed method is guaranteed to find a set of nodes with an influence of at least (1-1/e)OPT-2$\varepsilon$, where OPT is the optimal value. Experiments on both synthetic and real-world data show that the proposed method 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.

本文提出了一种基于随机算法来进行影响估计的方法，该方法可以在连续的时间传播网络中估计每个节点的影响力，通过大规模的实验数据表明，该方法不仅精度高，而且能够扩展到数百万个节点的网络上。

连续时间扩散网络中的可扩展影响估计