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bioRxiv · 10.1101/2020.01.28.922757

Spreading predictability in complex networks

Chen, D.-B.·Zhao, N.·Wang, J.·Yu, Y.·Zhao, J.
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Abstract

Spreading dynamics analysis is an important and interesting topic since it has many applications such as rumor or disease controlling, viral marketing and information recommending. Many state-of-the-art researches focus on predicting infection scale or threshold. Few researchers pay attention to the predicting of infection nodes from a snapshot. With developing of precision marketing, recommending and, controlling, how to predict infection nodes precisely from snapshot becomes a key issue in spreading dynamics analysis. In this paper, a probability based prediction model is presented so as to estimate the infection nodes from a snapshot of spreading. Experimental results on synthetic and real networks demonstrate that the model proposed could predict the infection nodes precisely in the sense of probability.

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Chen, D.-B., Zhao, N., Wang, J., Yu, Y., Zhao, J.. 2020-01-28. Spreading predictability in complex networks. https://doi.org/10.1101/2020.01.28.922757

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