Inductive and unsupervised graph learning is a critical technique for predictive or information retrieval tasks where label information is difficult to obtain. It is also challenging to make graph learning inductive and unsupervised at the same time, as learning processes guided by reconstruction error based loss functions inevitably demand graph similarity evaluation that is usually computationally intractable. In this paper, we propose a general framework SEED (Sampling, Encoding, and Embedding Distributions) for inductive and unsupervised representation learning on graph structured objects. Instead of directly dealing with the computational challenges raised by graph similarity evaluation, given an input graph, the SEED framework samples a number of subgraphs whose reconstruction errors could be efficiently evaluated, encodes the subgraph samples into a collection of subgraph vectors, and employs the embedding of the subgraph vector distribution as the output vector representation for the input graph. By theoretical analysis, we demonstrate the close connection between SEED and graph isomorphism. Using public benchmark datasets, our empirical study suggests the proposed SEED framework is able to achieve up to 10% improvement, compared with competitive baseline methods.
The authors propose a method for learning graph embeddings and focus specifically on a setting where not all graphs are part of the training data (the inductive setting). The core problem of graph embedding methods is to find a learnable function that maps arbitrary graphs into a fixed-sized vector representation. There have been several proposals ranging from the class of graph kernels to variations of graph neural networks. The authors propose a method that consists of three steps
(1) sample a number of subgraphs from the original graphs
(2) learn an encoding function for these subgraphs (subgraph -> vector representation)
(3) for every graph we, therefore, get a set of vector representations, one per subgraph. We now try to find a similarity measure operating on sets of vectors to compute the distance between graphs.
The novel bits are
(a) the way that the subgraphs are sampled (using an algorithm called WEAVE, that stores more information about random walks) and, therefore, is able to be distinguish graphs based on the extracted walks that standard random walk based methods cannot; and 
(b) to define a similarity measure based on the set of vectors. 
The authors also prove that their method is (under some assumptions) able to decide the isomorphism problem. This is a nice result to have in light of recent papers that have investigated the limitations of GNN in comparison to Weisfeiler-Leman and isomoprhism testing. 
Unfortunately, the proposed method has limited novelty. The WEAVE sampling is a small variation on random walk sampling that's been around for a while in graph representation learning. Also, to define the similarity between set of vectors has been addressed before in numerous papers (e.g., all papers investigating learning for sets, DeepSets, etc.) and the method here seems a bit ad-hoc and doesn't compare to existing work. 
My "novelty" critique is also made in light of the small number of datasets on which experiments have been conducted. If a new simple random walk strategy would lead to clearly better results on a number of datasets, this would be a significant contribution. As far as I can tell, however, the results are mixed and not very impressive especially due to the small number of datasets.