Many complex systems in the real world can be characterized by attributed
networks. To mine the potential information in these networks, deep embedded
clustering, which obtains node representations and clusters simultaneously, has
been paid much attention in recent years. Under the assumption of consistency
for data in different views, the cluster structure of network topology and that
of node attributes should be consistent for an attributed network. However,
many existing methods ignore this property, even though they separately encode
node representations from network topology and node attributes meanwhile
clustering nodes on representation vectors learnt from one of the views.
Therefore, in this study, we propose an end-to-end deep embedded clustering
model for attributed networks. It utilizes graph autoencoder and node attribute
autoencoder to respectively learn node representations and cluster assignments.
In addition, a distribution consistency constraint is introduced to maintain
the latent consistency of cluster distributions of two views. Extensive
experiments on several datasets demonstrate that the proposed model achieves
significantly better or competitive performance compared with the
state-of-the-art methods. The source code can be found at
this https URL.

本文提出了一种用于属性网络的端到端深度嵌入聚类模型，利用图自编码器和节点属性自编码器分别学习节点表示和聚类分配，并引入分布一致性约束来保持两个视图的聚类分布的潜在一致性。