Entropy-based approach to missing-links prediction

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 flexibilitymakes them strong competitors of available link-prediction algorithms.

Local-community network automata modelling based on length-three-paths for prediction of complex network structures in protein interactomes, food webs and more

“…The third important finding of this study is that also world international trade networks -for which it is known that the common neighbours principle is not effective [19] -follow a principle of self-organization according to which L3 is offering better link prediction performance than L2 ( Fig. 5 and Suppl.…”

Finding Missing Links in Complex Networks: A Multiple‐Attribute Decision‐Making Method

“…(9) Maximization entropy (MaxE) method [28]. This method is a likelihood-based algorithm based on a series of results concerning constrained entropy maximization [43].…”

“…Calculate the separation measures of potential missing links from the positive ideal and the negative ideal solutions. The separation measures between potential missing links and the positive ideal solution are calculated using (28), and the separation measures between potential missing links and the negative ideal solution are calculated using (29).…”

“…These values are acceptable. The configuration model of MaxE takes as input just the nodes degrees; networks with core-periphery structure seem to be largely explained by the degree sequences [28]. Thus, for networks with a core-periphery structure, the MaxE is suitable.…”

“…On the other hand, likelihood-based algorithms aim at identifying the most likely generative model for a network and then estimate the connection likelihood of any two nodes according to the considered model [28,29]. These algorithms usually assume that a network has a known structure and build a model, such as hierarchical structure model [29] and stochastic block model [9], to fit the structure and evaluate model parameters using statistical methods [4].…”