Reconstructing propagation with temporal similarity

Hao Liao and An Zeng*, Reconstructing propagation with temporal similarity, [Scientific Reports 5, 11404 (2015)]

简介：本文主要研究了传播网络的重构问题。与以往工作不同，本文使用经典的SIR传播模型。本文首先假设只有最终传播结果可知，而节点被感染的时间未知，在使用节点相似性对网络进行重构，发现大部分经典的相似性指标都能较好重构出传播网络。同时，我们发现若干相似性指标在重构网络时存在一些效果特别差的特殊参数区间。最后，我们将节点被感染时间信息加入经典的相似性指标，发现这种做法能大幅提高网络重构的精度。

摘要

Node similarity significantly contributes to the growth of real networks. In this paper, based on the observed epidemic spreading results we apply the node similarity metrics to reconstruct the underlying networks hosting the propagation. We find that the reconstruction accuracy of the similarity metrics is strongly influenced by the infection rate of the spreading process. Moreover, there is a range of infection rate in which the reconstruction accuracy of some similarity metrics drops nearly to zero. To improve the similarity-based reconstruction method, we propose a temporal similarity metric which takes into account the time information of the spreading. The reconstruction results are remarkably improved with the new method.

原文链接：http://www.nature.com/srep/2015/150618/srep11404/full/srep11404.html