Entropy-based approach to missing-links prediction

TL;DR

This paper introduces an entropy-based method for predicting missing links in networks, demonstrating superior accuracy and flexibility, including applicability to directed networks, compared to existing algorithms.

Contribution

The paper presents a novel entropy-based approach for missing link prediction that outperforms current methods and extends naturally to directed networks.

Findings

The entropy-based method achieves higher precision and ROC scores.

It outperforms popular algorithms on economic, financial, and food networks.

The approach is adaptable to directed networks, overcoming previous limitations.

Abstract

Link-prediction is an active research field within network theory, aiming at uncovering missing connections or predicting the emergence of future relationships from the observed network structure. This paper represents our contribution to the stream of research concerning missing links prediction. Here, we propose an entropy-based method to predict a given percentage of missing links, by identifying them with the most probable non-observed ones. The probability coefficients are computed by solving opportunely defined null-models over the accessible network structure. Upon comparing our likelihood-based, local method with the most popular algorithms over a set of economic, financial and food networks, we find ours to perform best, as pointed out by a number of statistical indicators (e.g. the precision, the area under the ROC curve, etc.). Moreover, the entropy-based formalism adopted in the present paper allows us to straightforwardly extend the link-prediction exercise to directed networks as well, thus overcoming one of the main limitations of current algorithms. The higher accuracy achievable by employing these methods - together with their larger flexibility - makes them strong competitors of available link-prediction algorithms.