Improving Network Inference: The Impact of False Positive and False Negative Conclusions about the Presence or Absence of Links

TL;DR

This paper investigates how false positive and false negative errors affect network inference accuracy, emphasizing the importance of balancing these errors through simulation to improve network topology estimation.

Contribution

It highlights the need to consider both false positives and negatives in network inference and proposes simulation-based tuning for more reliable network topology estimation.

Findings

Optimal error balance depends on network topology.

Existing methods focus on false positives, neglecting false negatives.

Simulation tuning improves network characteristic estimates.

Abstract

A reliable inference of networks from data is of key interest in the Neurosciences. Several methods have been suggested in the literature to reliably determine links in a network. To decide about the presence of links, these techniques rely on statistical inference, typically controlling the number of false positives, paying little attention to false negatives. In this paper, by means of a comprehensive simulation study, we analyse the influence of false positive and false negative conclusions about the presence or absence of links in a network on the network topology. We show that different values to balance false positive and false negative conclusions about links should be used in order to reliably estimate network characteristics. We propose to run careful simulation studies prior to making potentially erroneous conclusion about the network topology. Our analysis shows that optimal values to balance false positive and false negative conclusions about links depend on the network topology and characteristic of interest. Existing methods rely on a choice of the rate for false positive conclusions. They aim to be sure about individual links rather than the entire network. The rate of false negative conclusions is typically not investigated. Our investigation shows that the balance of false positive and false negative conclusions about links in a network has to be tuned for any network topology that is to be estimated. Moreover, within the same network topology, the results are qualitatively the same for each network characteristic, but the actual values leading to reliable estimates of the characteristics are different.