Cascade source inference in networks: a Markov chain Monte Carlo approach

Abstract

Cascades of information, ideas, rumors, and viruses spread through networks. Sometimes, it is desirable to find the source of a cascade given a snapshot of it. In this paper, source inference problem is tackled under Independent Cascade (IC) model. First, the #P-completeness of source inference problem is proven. Then, a Markov chain Monte Carlo algorithm is proposed to find a solution. It is worth noting that our algorithm is designed to handle large networks. In addition, the algorithm does not rely on prior knowledge of when the cascade started. Finally, experiments on real social network are conducted to evaluate the performance. Under all experimental settings, our algorithm identified the true source with high probability.

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Cite this paper

@inproceedings{Zhai2015CascadeSI, title={Cascade source inference in networks: a Markov chain Monte Carlo approach}, author={Xuming Zhai and Weili Wu and Wen Xu}, year={2015} }