Link prediction by analyzing temporal behavior of vertices

Abstract

Complexity and dynamics are challenging properties of real-world social networks. Link prediction in dynamic social networks is an essential problem in social network analysis. Although different methods have been proposed to enhance the performance of link prediction, these methods need significant improvement in accuracy. In this study, we focus on the temporal behavior of social networks to predict potential future interactions. We examine the evolving pattern of vertices of a given network $$\mathcal {G}$$ over time. We introduce a time-varying score function to evaluate the activeness of vertices that uses the number of new interactions and the number of frequent interactions with existing connections. To consider the impact of timestamps of the interactions, the score function engages a time difference of the current time and the time of the interaction occurred. Many existing studies ignored the weight of the link in the given network $$\mathcal {G}$$, which brings the time-varied details of the links. We consider two additional objective functions in our model: a weighted shortest distance between any two nodes and a weighted common neighbor index. We used Multi-Layer Perceptron (MLP), a deep learning architecture as a classifier to predict the link formation in the future and define our model as a binary classification problem. To evaluate our model, we train and test with six real-world dynamic networks and compare it with state-of-the-art methods as well as classic methods. The results confirm that our proposed method outperforms most of the state-of-the-art methods.