Link prediction of time-evolving network based on node ranking

Abstract

Many real-world networks belong to the kind that evolves over time. So it is very meaningful and challenging to predict whether the link will occur in the network of future time. In this paper, both time-evolving scale-free (SF) network and real-world dynamic network are taken into consideration first and then two kinds of methods are respectively proposed for link prediction. Different from many existing similarity-based dynamic network link prediction methods, many of which adopt node-pair similarity such as common neighbors (CN), Adamic–Adar (AA), and so on, we measure the similarity between nodes from a new perspective. With further research into node ranking, some eigenvector-based methods, such as PageRank (PR), Cumulative Nomination (CuN) and so on, can compute the values of node importance which can be regarded as the stationary distribution of Markov chain for all nodes iteratively. Therefore, from a statistical point of view, the importance of a node is like the probability of attracting other nodes to connect with it and the derivative value of a node pair is like the probability of attracting each other. These node-ranking-based approaches are very novel in the field of link prediction in that few researches have paid enough attention to them before. In addition, an adaptively time series forecasting method is proposed in this paper, and it uses the historical similarity series to predict the future similarity between each node pair adaptively. Experimental results show that our proposed algorithms can predict the future links not only for the growing SF network but also for the dynamic networks in the real-world.