Impact of network characteristics on network reconstruction

Abstract

When a network is inferred from data, two types of errors can occur: false positive and false negative conclusions about the presence of links. We focus on the influence of local network characteristics on the probability $\alpha$ - of type I false positive conclusions, and on the probability $\beta$ - of type II false negative conclusions, in the case of networks of coupled oscillators. We demonstrate that false conclusion probabilities are influenced by local connectivity measures such as the shortest path length and the detour degree, which can also be estimated from the inferred network when the true underlying network is not known a priory. These measures can then be used for quantification of the confidence level of link conclusions, and for improving the network reconstruction via advanced concepts of link thresholding.

References in corpus (1)

• Reconstruction of a random phase dynamics network from observations

Cited by in corpus (1)

• Machine Learning Link Inference of Noisy Delay-coupled Networks with Opto-Electronic Experimental Tests