The current research of link prediction mainly focuses on single node pair link prediction for social network

in which the topology doesn't change frequently. In this paper

for the opportunistic network with frequent topology change

we propose a multi-nodes link prediction method based on pattern classification. This method employs chaotic time series theory to determine the slicing time of opportunistic network

and the topology of the network is depicted by the state diagram. The structural features of opportunistic network can be extracted from the evolution of the state diagram in terms of the advantages of the deep convolution neural network on the feature extraction. The evolution pattern of the future link is inferred from the current features so as to realize the multi-nodes link prediction. The experimental results on the Imote-Traces-Cambridge dataset show that the proposed method has better precision and stability than the prediction methods based on CN (Common Neighbor)

Adamic-Adar and Katz.