Link prediction in dynamic networks using time-aware network embedding and time series forecasting

As most real-world networks evolve over time, link prediction over such dynamic networks has become a challenging issue. Recent researches focus towards network embedding to improve the performance of link prediction task. Most of the network embedding methods are only applicable to static networks and therefore cannot capture the temporal variations of dynamic networks. In this work, we propose a time-aware network embedding method which generates node embeddings by capturing the temporal dynamics of evolving networks. Unlike existing works which use deep architectures, we design an evolving skip-gram architecture to create dynamic node embeddings. We use the node embedding similarities between consecutive snapshots to construct a univariate time series of node similarities. Further, we use times series forecasting using auto regressive integrated moving average (ARIMA) model to predict the future links. We conduct experiments using dynamic network snapshot datasets from various domains and demonstrate the advantages of our system compared to other state-of-the-art methods. We show that, combining network embedding with time series forecasting methods can be an efficient solution to improve the quality of link prediction in dynamic networks.