Reconstructing propagation networks with temporal similarity metrics

TL;DR

This paper introduces a temporal similarity metric to improve the reconstruction of propagation networks from spreading data, addressing limitations of traditional similarity metrics affected by infection rates.

Contribution

The paper proposes a novel temporal similarity metric that significantly enhances the accuracy of reconstructing propagation networks from spreading results.

Findings

Reconstruction accuracy varies with infection rate.

A specific infection rate range causes near-zero accuracy with traditional metrics.

Temporal similarity metrics markedly improve reconstruction results.

Abstract

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.