Sign Prediction on Social Networks Based Nodal Features

Abstract

The sentiments among social individuals are complexity and diversity, and the relationships between them include being friendly and hostile. The positive ("friendly","like" or "trust") or negative ("hostile", "dislike" or "distrust") sentiments in the relations can be modeled as signed connections or links. The missing relations or sentiments between individuals are always worthy of speculation. The sign predication on links has been significant applications in a variety of online settings, such as online recommendation system and abnormal user detections. A novel sign prediction method called the SPR model is measured by the values of the two indexes, one is similarity; the other is preference-reputation (PR). The similarity of a pair nodes is defined by the statistical properties of local structures. The definition of similarity agrees with the theory of social balance because existing connections reflect the tendency of the new links emergence between individuals. And PR value is to measure the positive or negative tendency of edges without sign. The experiments on real big social data proved the feasibility and efficiency of the SPR model: Comparing with some popular predication methods, the SPR model in this issue shows lower complexity and higher accuracy. Experimental results also prove that the SPR model provide insight and foresight of the mechanism driving the sign formation of links.