Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 3: Weak Reject
review_content: The paper proposes an unsupervised approach to learn a representation of graphs. The idea comes from an encoder-decoder architecture, which is common in related literature. The paper uses a variational Bayes approach in the learning process. Thorough experiments are provided to justify the feasibility of the method. 

This paper provides an unsupervised style of learning graph representations, which may not be coupled with a specific downstream task so that it may be more useful in general; also, the experiments themselves seem to be at least comparable to the recent methods.  

However, I vote for rejecting this submission for the following concerns. 

(1) I did not find too many significant differences between this paper and [Kingma & Welling, 2014] in the design of encoder-decoder architecture as well as the learning procedure (I am not an expert in this area so please correct me if I am wrong).

(2) The intuition of learning the representation in an unsupervised manner is interesting and important to me, though the experiments are mostly on the classification tasks. I think it would be helpful to demonstrate the representation power of the learned representation of the graph in tackling other tasks.

review_title: Official Blind Review #2

final_score: rating: 6: Weak Accept
review_content: The authors propose in this paper a new unsupervised graph representation learning method. The method leverages recent advances in graph coarsening, mainly Loukas' method. The key idea of the method consists in using a reconstruction target that is not the classical one in an auto-encoder setting. More precisely, the encoder takes as an input the original adjacency matrix and node features but the decode only aims at reconstructing the coarse adjacency matrix (obtained via Loukas' method). 

The experimental evaluation is quite thorough and shows that the method performs quite well, especially considering it is unsupervised but is compared to supervised representation methods. It would be nice to include statistical tests to assess the significance of the differences in cases were accuracies are very close one to another. A missing part would be to explore the relevance of the learned representation for other tasks (i.e. to use a multi task data set). Of course as the representation is learned in an unsupervised way, one can argue that the current evaluation is already providing an answer.

Overall, I find the paper clear, but the variational bayes part could be much clearer. In fact I'm not sure why this is presented as variational bayes and not only variational. I do not see any prior distribution over parameters, for instance. I understand that the recent "tradition" in variational auto-encoder is to use this terminology, but as a (part time) bayesian, this is a bit annoying. 
review_title: Official Blind Review #1

final_score: rating: 3: Weak Reject
review_content: Summary: This work proposes an unsupervised hierarchical graph representation learning method, named BayesPool. The method learns a coarsening sequence of graphs together with the corresponding node representations. The coarsening sequence is learned using the method in Loukas (2019). The node representations are learned using an encoder-decoder structure, where the encoder encodes a graph to coarsened node representations, and the decoder decodes the node representations to a coarsened graph. The adopted objective function is analogous to VAE, except that the decoder does not aims to reconstruct an identical graph. Experiments on graph classification is performed on 5 different datasets, and competitive accuracy is achieved.

Concerns: The authors claim that the leant representation in an unsupervised manner is more desirable in terms of generalization. However, they only provide very limited experimental results, which is not very convincing. Moreover, the authors also do not explain clearly on when the node representation of the coarsening sequence is needed.
review_title: Official Blind Review #3

Meta-review: The paper presents an unsupervised method for graph representation, building upon Loukas' method for generating a sequence of gradually coarsened graphs. The contribution is an "encoder-decoder" architecture trained by variational inference, where the encoder produces the embedding of the nodes in the next graph of the sequence, and the decoder produces the structure of the next graph. 

One important merit of the approach is  that this unsupervised representation can be used effectively for supervised learning, with results quite competitive to the state of the art. 

However the reviewers were unconvinced by the novelty and positioning of the approach. The point of whether the approach should be viewed as variational Bayesian, or simply variational approximation was much debated between the reviewers and the authors. 

The area chair encourages the authors to pursue this very promising research, and to clarify the paper; perhaps the use of "encoder-decoder" generated too much misunderstanding. 
Another graph NN paper you might be interested in is "Edge Contraction Pooling for Graph NNs", by Frederik Diehl. 

Decision: Reject