Abstract: As one of the most basic tasks in data mining and machine learning,clustering is widely used in various real-world tasks.With the development of deep learning deep clustering has become a research hotspot.Existing deep clustering algorithms are mainly from two aspects of node representation learning or structural representation learning.Less work considers fusing these two kinds of information at the same time to complete representation learning.This paper proposes a deep clustering model FVGTAEDC (Deep Clustering Model Based on Fusion Varitional Graph Attention Self-encoder),this model joints the autoencoderand the variational graph attention autoencoder for clustering.In the model,the autoencoder integrates the variational graph attention autoencoder from the network to learn (low-order and high-order) structural representations,and then the feature representation is learned from the original data.While the two modules are trained,in order to adapt to the clustering task,self-supervised clustering training for the autoencoder module is integrated with the representation features of the node and the structure information.Comprehensive clustering loss,autoencoder reconstruction data loss,and variational graph attention autoencoder reconstruction adjacency matrix loss,the relative entropy loss of the posterior probability distribution and the prior probability distribution.The method can effectively aggregate the attributes of nodes and the structure of the network,while optimizing the assignment of cluster labels and learning the representation features suitable for clustering.Comprehensive experiments prove that the method is better than the current advanced deep clustering method on 5 real data.

Key words: Deep clustering, Representation learning, Self encoder, Self-supervised clustering, Variational graph attention self-encoder