The combination of the traditional convolutional network (i.e., an auto-encoder) and the graph convolutional network has attracted much attention in clustering, in which the auto-encoder extracts the node attribute feature and the graph convolutional network captures the topological graph feature. However, the existing works (i) lack a flexible combination mechanism to adaptively fuse those two kinds of features for learning the discriminative representation and (ii) overlook the multi-scale information embedded at different layers for subsequent cluster assignment, leading to inferior clustering results. To this end, we propose a novel deep clustering method named Attention-driven Graph Clustering Network (AGCN). Specifically, AGCN exploits a heterogeneity-wise fusion module to dynamically fuse the node attribute feature and the topological graph feature. Moreover, AGCN develops a scale-wise fusion module to adaptively aggregate the multi-scale features embedded at different layers. Based on a unified optimization framework, AGCN can jointly perform feature learning and cluster assignment in an unsupervised fashion. Compared with the existing deep clustering methods, our method is more flexible and effective since it comprehensively considers the numerous and discriminative information embedded in the network and directly produces the clustering results. Extensive quantitative and qualitative results on commonly used benchmark datasets validate that our AGCN consistently outperforms state-of-the-art methods.

翻译：传统的革命网络(即自动编码器)和图形革命网络的结合在集群中引起了人们的极大注意,在集群中,自动编码器提取节点属性特性,而图形革命网络捕捉了表层图形特性;然而,现有的工作(一) 缺乏一种灵活的组合机制,以适应性地结合这两种特性,用于学习歧视性代表性,(二) 忽视不同层次的多层次信息,用于随后的集群任务,导致低级集群结果。为此,我们建议采用一种新的深层次集群方法,名为“注意驱动的图表组合网络”(AGCN)。具体地说,AGCN利用一种异质性感融合模块,动态地结合节点属性特性和表层图特征。此外,AGCN开发了一个规模化的混合模块,以适应性地整合不同层次所嵌入的多尺度特征。基于统一的优化框架,AGCN可以联合进行特征学习和集群任务,与现有的深度集群方法相比,我们采用的方法更加灵活,并且更能持续地将我们所使用的定性结果整合在一起。