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Deep graph embedding is an important approach for community discovery. Deepgraph neural network with self-supervised mechanism can obtain thelow-dimensional embedding vectors of nodes from unlabeled and unstructuredgraph data. The high-order information of graph can provide more abundantstructure information for the representation learning of nodes. However, mostself-supervised graph neural networks only use adjacency matrix as the inputtopology information of graph and cannot obtain too high-order informationsince the number of layers of graph neural network is fairly limited. If thereare too many layers, the phenomenon of over smoothing will appear. Thereforehow to obtain and fuse high-order information of graph by a shallow graphneural network is an important problem. In this paper, a deep graph embeddingalgorithm with self-supervised mechanism for community discovery is proposed.The proposed algorithm uses self-supervised mechanism and different high-orderinformation of graph to train multiple deep graph convolution neural networks.The outputs of multiple graph convolution neural networks are fused to extractthe representations of nodes which include the attribute and structureinformation of a graph. In addition, data augmentation and negative samplingare introduced into the training process to facilitate the improvement ofembedding result. The proposed algorithm and the comparison algorithms areconducted on the five experimental data sets. The experimental results showthat the proposed algorithm outperforms the comparison algorithms on the mostexperimental data sets. The experimental results demonstrate that the proposedalgorithm is an effective algorithm for community discovery.

Self-Supervised Deep Graph Embedding with High-Order Information Fusion for Community Discovery