  Graph clustering (or community detection) has long drawn enormous attention
from the research on web mining and information networks. Recent literature on
this topic has reached a consensus that node contents and link structures
should be integrated for reliable graph clustering, especially in an
unsupervised setting. However, existing methods based on shallow models often
suffer from content noise and sparsity. In this work, we propose to utilize
deep embedding for graph clustering, motivated by the well-recognized power of
neural networks in learning intrinsic content representations. Upon that, we
capture the dynamic nature of networks through the principle of influence
propagation and calculate the dynamic network embedding. Network clusters are
then detected based on the stable state of such an embedding. Unlike most
existing embedding methods that are task-agnostic, we simultaneously solve for
the underlying node representations and the optimal clustering assignments in
an end-to-end manner. To provide more insight, we theoretically analyze our
interpretation of network clusters and find its underlying connections with two
widely applied approaches for network modeling. Extensive experimental results
on six real-world datasets including both social networks and citation networks
demonstrate the superiority of our proposed model over the state-of-the-art.
