Entropy-based approach to missing-links prediction.

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 and financial networks, we find ours to perform best, as pointed out by a number of statistical indicators like the precision or the area under the ROC curve. The higher accuracy achievable by employing these methods together with their larger flexibility (being they applicable to all kinds of network structures, be they directed, weighted, bipartite) make them strong competitors of available link-prediction algorithms.