Link Prediction Based on Time Series of Similarity Coefficients and Structural Function

A social network is a structure whose nodes represent people or other entities embedded in a social context while its edges symbolize interaction, collaboration or exertion of influence between these fore-mentioned entities [3]. From a wide class of problems related to social networks, the ones related to link dynamics seems particularly interesting. A noteworthy link prediction technique, based on analyzing the history of the network (i.e. its previous states), was presented by Prudêncio and da Silva Soares in [5]. In this paper, we attempt to improve the quality of edges’ formation prognosis in social networks by proposing a modified version of aforementioned method. For that purpose we shall compute values of certain similarity coefficients and use them as an input to a supervised classification mechanism (called structural function). We stipulate that this function changes over time, thus making it possible to derive time series for all of its parameters and obtain their next values using a forecasting model. We might then predict new links’ occurrences using the forecasted values of similarity metrics and supervised classification method with the predicted parameters. This paper contains also the comparison of ROC charts for both legacy solution and the novel method.