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Scalable influence estimation in continuous-time diffusion networks (2013)
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| Venue: | In |
| Citations: | 23 - 6 self |
BibTeX
@INPROCEEDINGS{Du13scalableinfluence,
author = {Nan Du and Le Song and Manuel Gomez-rodriguez and Hongyuan Zha},
title = {Scalable influence estimation in continuous-time diffusion networks},
booktitle = {In},
year = {2013}
}
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Abstract
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 |V | nodes and |E | edges to an accuracy of using n = O(1/2) randomizations and up to logarithmic factorsO(n|E|+n|V|) computations. When used as a subroutine in a greedy influence maximization approach, our proposed algorithm is guaranteed to find a set of C nodes with the influence of at least (1 − 1/e) OPT−2C, 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. 1
