  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.
