基于时间相似性度量的传播网络重构
Reconstructing propagation networks with temporal similarity metrics
摘要：
节点相似性是驱动真实网络增长的重要属性。在本文中，基于观察到的传播结果，我们应用节点相似性度量来重建传播网络。我们发现，重建精度的相似性度量的传播过程的感染率的强烈影响。此外，有一个范围内的感染率，其中一些相似性度量的重建精度下降到接近零。为了改进基于相似性的重建方法，我们最后提出了一个时间相似性度量，考虑到时间信息的传播。新方法的重建效果得到了显著改善。
英文摘要：
Node similarity is a significant property driving the growth of real networks. In this paper, based on the observed spreading results we apply the node similarity metrics to reconstruct propagation networks. 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 to nearly zero. In order to improve the similarity-based reconstruction method, we finally propose a temporal similarity metric to take into account the time information of the spreading. The reconstruction results are remarkably improved with the new method.
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