  Graph representation learning based on graph neural networks (GNNs) can
greatly improve the performance of downstream tasks, such as node and graph
classification. However, the general GNN models do not aggregate node
information in a hierarchical manner, and can miss key higher-order structural
features of many graphs. The hierarchical aggregation also enables the graph
representations to be explainable. In addition, supervised graph representation
learning requires labeled data, which is expensive and error-prone. To address
these issues, we present an unsupervised graph representation learning method,
Unsupervised Hierarchical Graph Representation (UHGR), which can generate
hierarchical representations of graphs. Our method focuses on maximizing mutual
information between "local" and high-level "global" representations, which
enables us to learn the node embeddings and graph embeddings without any
labeled data. To demonstrate the effectiveness of the proposed method, we
perform the node and graph classification using the learned node and graph
embeddings. The results show that the proposed method achieves comparable
results to state-of-the-art supervised methods on several benchmarks. In
addition, our visualization of hierarchical representations indicates that our
method can capture meaningful and interpretable clusters.
