  Graph-based clustering plays an important role in the clustering area. Recent
studies about graph convolution neural networks have achieved impressive
success on graph type data. However, in general clustering tasks, the graph
structure of data does not exist such that the strategy to construct a graph is
crucial for performance. Therefore, how to extend graph convolution networks
into general clustering tasks is an attractive problem. In this paper, we
propose a graph auto-encoder for general data clustering, which constructs the
graph adaptively according to the generative perspective of graphs. The
adaptive process is designed to induce the model to exploit the high-level
information behind data and utilize the non-Euclidean structure sufficiently.
We further design a novel mechanism with rigorous analysis to avoid the
collapse caused by the adaptive construction. Via combining the generative
model for network embedding and graph-based clustering, a graph auto-encoder
with a novel decoder is developed such that it performs well in weighted graph
used scenarios. Extensive experiments prove the superiority of our model.
