ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2022, Vol. 59 ›› Issue (4): 834-851.doi: 10.7544/issn1000-1239.20200673

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Node Importance Estimation Method for Opportunistic Network Based on Graph Neural Networks

Liu Linlan1, Tan Zhenyang1, Shu Jian2   

  1. 1(School of Information Engineering, Nanchang Hangkong University, Nanchang 330063);2(School of Software, Nanchang Hangkong University, Nanchang 330063)
  • Online:2022-04-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (62062050, 61962037) and the Natural Science Foundation of Jiangxi Province (20202BABL202039).

Abstract: Opportunistic network is a type of self-organized networks which uses the opportunity of a node moving to realize communication.Because of opportunistic communication mode, opportunistic network has observable time-varying and dynamic characteristics.The estimation of node importance is the key to study the information dissemination of opportunistic network.A novel node importance estimation method based on graph neural network (GNN-NIE) framework is proposed.Opportunistic network is sliced into opportunistic network units which is modeled by aggregate graph to present network information.The dynamic network embedding model is employed to extract the temporal and structural information among the opportunistic network units, so as to obtain the dynamic attribute features of each node in the network.Taking advantage of the GNN’s ability of extracting the features of graph data, the relationship between node dynamic attribute features and the node importance is achieved, so that the node importance of opportunistic network is estimated.The results on three real opportunistic network datasets MIT reality, Haggle project and Asturias-er show that compared with the temporal degree, temporal betweenness, temporal PageRank, and kshell-CN, the proposed method has faster propagation rate, larger message coverage and better SIR and NDCG@10 values.

Key words: opportunistic network, node importance, network embedding, graph neural network, message propagation rate

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