  Recent years have seen a rise in the development of representational learning
methods for graph data. Most of these methods, however, focus on node-level
representation learning at various scales (e.g., microscopic, mesoscopic, and
macroscopic node embedding). In comparison, methods for representation learning
on whole graphs are currently relatively sparse. In this paper, we propose a
novel unsupervised whole graph embedding method. Our method uses spectral graph
wavelets to capture topological similarities on each k-hop sub-graph between
nodes and uses them to learn embeddings for the whole graph. We evaluate our
method against 12 well-known baselines on 4 real-world datasets and show that
our method achieves the best performance across all experiments, outperforming
the current state-of-the-art by a considerable margin.
