| The important nodes in the complex network are of great significance for preventing rumors,controlling the spread of public opinion,and maximizing social influence.The research on the importance of nodes is one of the hot areas of complex networks.Many scholars have conducted in-depth research on complex networks and have put forward many achievements.In recent years,some scholars have begun to pay attention to temporal networks,and have studied the structural characteristics and dynamic evolution of temporal networks.Research on sexual discovery,the specific research content is as follows.A method for studying the importance of time series network nodes based on feature vectors is proposed.First,the temporal network is divided into discrete time windows according to the time window model,and the corresponding adjacency matrix feature vectors of each window are calculated.Second,the rate of change between each time window is calculated to retain the timing information between adjacent windows.The simple function method processes the above two results to calculate the importance of each node in the temporal network.Finally,the real temporal network data set is used to experiment the proposed algorithm.The experimental results show that the proposed algorithm is more reasonable than other algorithms sort results.A method for studying the importance of temporal network nodes based on sliding windows is proposed.Firstly,use the sliding window model to calculate the moving average of each window and assign it to the nodes in the window,reflecting the continuous time series information of the temporal network;secondly,calculate the importance of the temporal network node through the moving average combined with the K-shell decomposition method;finally Experiments with real temporal network data sets and other algorithms,the results can be obtained by this method has a more reasonable ranking results than other algorithms.In this thesis,there are 33 figures,17 tables and 85 references. |
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