This paper consists of two parts. Firstly, we specify a general framework for constructing systems capable of learning distributed representations of graphs in an unsupervised manner. We show how systems such as Deep Graph Kernels, Graph2Vec and Anonymous Walk Embeddings can be formulated under this framework. In the second part of this paper, we exemplify the construction of an instance of this framework by proposing a model which extends Graph2Vec, called G2DR. This model extends Graph2Vec's current implementation to enable application on unlabelled graphs and tackles issues of diagonal dominance through pruning of the substructure vocabulary. These extensions help G2DR achieve state of the art results in downstream application of learned embeddings in graph classification tasks over datasets with small labeled graphs in binary classification to multiclass classification on large unlabelled graphs using an off-the-shelf support vector machine.
Strength:
-- The paper is well written and easy to follow
--  Learning the unsupervised graph representation learning is a very important problem
-- The proposed approach seems effective on some data sets.
Weakness:
-- The novelty of the proposed approach is very marginal
-- The experiments are very weak. 
This paper studied unsupervised graph representation learning. The authors combined the techniques for Deep Graph Kernels and Graph2Vec, which essential extract substructures as words and the whole graph as documents and use doc2vec for learning the representations of both graphs and substructures. Experimental results on a few data sets prove the effectiveness of the proposed approach. 
Overall, the paper is well written and easy to follow. Learning unsupervised graph representation learning is a very important problem, especially for predicting the chemical properties of molecular structures. However, the novelty of the proposed method is very marginal. Comparing to the Deep Graph kernel methods, the authors simply changed from the word2vec style methods to doc2vec style methods. The paper could be better fit to a more applied conference. Moreover,  I have some concerns on the experiments. 
(1) The data sets used in this paper are too small. For unsupervised pretraining methods, much larger data sets are expected. 
(2) The results in Table 1 are really weird. Why do the performance of your method have a much lower standard deviation? It is really hard to believe the proposed methods have much stable performance compare to other methods.  Can you explain this?