Analysis of Complex Network Based on Second-order Graph Autoencoder
In order to make full use of the information contained in complex networks and enhance the representation ability of graph autoencoder models,we propose an autoencoder model SeGCN-AE based on Second-order Graph Convolutional Networks(SeGCN).First,SeGCN is used to extract entity attributes and relationship information,and generate low-dimensional feature representations.Then,the inner product decoder is used to reconstruct the complex network link relationship matrix,and the model is optimized by reconstruction loss.On the two baseline complex network dataset experiments,the performance of SeGCN-AE is always better than current advanced baseline model,indicating that the introduction of second-order relationships can enhance representation ability of the model and improve the performance of complex network analysis tasks.
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