Abstract: Link prediction in temporal networks was analyzed and discussed in detail. The temporal network was divided into multilayer network snapshot sequences with the same time in chronological order. Aiming at solving the problem of rough granularity obtained by the common-neighbor-based similarity index， similarity indexes NCC and NCCP based on neighbor node clustering coefficient were proposed. Then a link prediction algorithm for temporal networks was designed for networks based on these two indicators. The comparison experiments on real datasets showed that the cluster information of neighbor nodes can improve the prediction accuracy. The superiority of the proposed link prediction algorithm was verified by a real mail dataset， and the experimental results showed that the closer the network structure is to the prediction time， the greater the impact on the prediction results.

Key words: temporal networks, link prediction, multilayer network, clustering, similarity