Temporal preferential attachment: Predicting new links in temporal social networks

Abstract

Social networks have shown an exponential growth in the recent past. It has estimated that nearly 4 billion people are currently using social networks. The growth of social networks can be explained using different models. Preferential Attachment (PA) is a widely used model, which is often used to link prediction in social networks. PA tells that the social network users prefer to get linked with popular users in the network. However, the popularity of a node depends not only on the node’s degree but also on the node's activeness which is reflected by the amount of active links the node has at present. Activeness of a link can be quantified using the timestamp of the link. The present work introduces a novel method called Temporal Preferential Attachment (TPA) which is defined on the activeness and strength of a node. Strength of a node is the sum of weights of links attached to the node. Here, the weights of the links are assigned according to their activeness. Thus, TPA captures the temporal behaviors of nodes, which is a vital factor for new link formation. The novel method uses min - max scaling to scale the time differences between current time and the timestamps of the links. Here, the min value is the earliest timestamp of the links in the given network and max value is the latest timestamp of the links. The scaled time difference of a link is considered as the temporal weight of the link, which reflects its activeness. TPA was evaluated in terms of its link prediction performance using well-known social network data sets. The results show that TPA performs well in link prediction compared to PA, and show a significant improvement in prediction accuracy.