%0 Journal Article
%A CHEN Yubin
%A SONG Xiuyang
%A LIU Linlan
%T Link Prediction in Opportunistic Networks Based on Network Representation Learning
%D 2022
%R 10.13190/j.jbupt.2021-211
%J Journal of Beijing University of Posts and Telecommunications
%P 64-69,103
%V 45
%N 4
%X According to the characteristics of topology frequent changes and multi-dimensional attributes in opportunistic networks, a link prediction method based on network representation learning is proposed. The opportunistic network is transformed into snapshots by setting time slot. The link state of each snapshot is represented by multi-dimensional link attributes. Then, the network representation learning method is adopted to aggregate the multi-dimensional link attributes of neighbor nodes, which are mapped into a low-dimensional embedding matrix. The recurrent neural network improved based on the attention mechanism is employed to learn the laws of the evolution of network topology, and to extract the timing features between embedding matrices. Through the output layers, the mapping relationship between time serial characteristics and link-state is established to implement the link prediction for network at the next moment. The experimental results on mainstream datasets, such as Infocom-05 and Hyccups show that the proposed method achieves higher prediction accuracy compared with the existing link prediction methods.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2021-211