  Unsupervised graph representation learning aims to learn low-dimensional node
embeddings without supervision while preserving graph topological structures
and node attributive features. Previous graph neural networks (GNN) require a
large number of labeled nodes, which may not be accessible in real-world graph
data. In this paper, we present a novel cluster-aware graph neural network
(CAGNN) model for unsupervised graph representation learning using
self-supervised techniques. In CAGNN, we perform clustering on the node
embeddings and update the model parameters by predicting the cluster
assignments. Moreover, we observe that graphs often contain inter-class edges,
which mislead the GNN model to aggregate noisy information from neighborhood
nodes. We further refine the graph topology by strengthening intra-class edges
and reducing node connections between different classes based on cluster
labels, which better preserves cluster structures in the embedding space. We
conduct comprehensive experiments on two benchmark tasks using real-world
datasets. The results demonstrate the superior performance of the proposed
model over existing baseline methods. Notably, our model gains over 7%
improvements in terms of accuracy on node clustering over state-of-the-arts.
