Abstract: Deep graph embedding is an important approach for community discovery. Deep
graph neural network with self-supervised mechanism can obtain the
low-dimensional embedding vectors of nodes from unlabeled and unstructured
graph data. The high-order information of graph can provide more abundant
structure information for the representation learning of nodes. However, most
self-supervised graph neural networks only use adjacency matrix as the input
topology information of graph and cannot obtain too high-order information
since the number of layers of graph neural network is fairly limited. If there
are too many layers, the phenomenon of over smoothing will appear. Therefore
how to obtain and fuse high-order information of graph by a shallow graph
neural network is an important problem. In this paper, a deep graph embedding
algorithm with self-supervised mechanism for community discovery is proposed.
The proposed algorithm uses self-supervised mechanism and different high-order
information of graph to train multiple deep graph convolution neural networks.
The outputs of multiple graph convolution neural networks are fused to extract
the representations of nodes which include the attribute and structure
information of a graph. In addition, data augmentation and negative sampling
are introduced into the training process to facilitate the improvement of
embedding result. The proposed algorithm and the comparison algorithms are
conducted on the five experimental data sets. The experimental results show
that the proposed algorithm outperforms the comparison algorithms on the most
experimental data sets. The experimental results demonstrate that the proposed
algorithm is an effective algorithm for community discovery.