Link prediction in dynamic networks based on the attraction force between nodes

摘要

As an important technology of social network analysis, link prediction is widely applied in computer science and many other fields. Link prediction can be used to detect missing links or predict whether two unconnected nodes will connect in the future. Various link prediction approaches have been proposed based on similarity metrics or learning in recent years: however, most failed to consider the direct changes during network development, and hence they are not applied to dynamic networks whose structures change continuously over time. In this paper, a novel approach for link prediction in dynamic networks based on the attraction force between nodes (DLPA) is proposed for detecting missing links and for predicting whether potential links will become real links in the future. First, a level is assigned to each node, which is used to represent the influence strength of the node compared to its neighbours in the initial network snapshot. The level must be updated with changes in the nodes. Then, the connection probability of each potential link is calculated based on the levels of the corresponding nodes and the attraction force between them. Thus, missing links can be detected and potential links can be predicted. In addition, the connection probabilities of potential links calculated via the proposed approach can vary with the evolution of the network. Experiments on static and dynamic real-world networks are conducted to evaluate the performance of the proposed approach, and the results demonstrate that the proposed approach outperforms several baseline algorithms in terms of prediction accuracy. (C) 2019 Elsevier B.V. All rights reserved.