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.

许多现实世界的网络都属于随时间演变的网络。因此，预测链接是否会在将来的时间网络中发生是非常有意义且具有挑战性的。本文首先考虑了时变无标尺（SF）网络和现实世界的动态网络，然后分别提出了两种方法进行链路预测。与许多现有的基于相似度的动态网络链接预测方法不同，许多方法采用节点对相似度，例如公共邻居（CN），Adamic-Adar（AA）等，我们从新的角度衡量节点之间的相似度。随着对节点排序的进一步研究，一些基于特征向量的方法，例如PageRank（PR），累积提名（CuN）等，可以迭代地计算节点重要性的值，该值可以被视为马尔可夫链的平稳分布。因此，从统计的角度来看，一个节点的重要性就像吸引其他节点与之连接的概率，而节点对的派生值就像吸引彼此的概率。这些基于节点排序的方法在链接预​​测领域中是非常新颖的，因为之前很少有研究对其进行足够的关注。此外，本文提出了一种自适应时间序列预测方法，该方法利用历史相似度序列来自适应预测每个节点对之间的未来相似度。