  Recently a variety of methods have been developed to encode graphs into
low-dimensional vectors that can be easily exploited by machine learning
algorithms. The majority of these methods start by embedding the graph nodes
into a low-dimensional vector space, followed by using some scheme to aggregate
the node embeddings. In this work, we develop a new approach to learn
graph-level representations, which includes a combination of unsupervised and
supervised learning components. We start by learning a set of node
representations in an unsupervised fashion. Graph nodes are mapped into node
sequences sampled from random walk approaches approximated by the
Gumbel-Softmax distribution. Recurrent neural network (RNN) units are modified
to accommodate both the node representations as well as their neighborhood
information. Experiments on standard graph classification benchmarks
demonstrate that our proposed approach achieves superior or comparable
performance relative to the state-of-the-art algorithms in terms of convergence
speed and classification accuracy. We further illustrate the effectiveness of
the different components used by our approach.
