Review for NeurIPS paper: Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models

Weaknesses: While the problem setting and proposed approach are interesting there are some drawbacks in the execution of this idea. First much of the experimental detail is left to the supplementary material and makes the main paper appear lacking in results. Concerningly, few of the transductive baselines outperform the main baselines (see Cora table in the Appendix for Deepwalk features) reported in the main body of the paper and thus their omission is questionable. Furthermore, the chosen datasets as the paper recognizes are either small graphs or contain only a single graph and as a result its difficult to assess how scalable the proposed approach is to larger real world graphs. The biggest weakness in this reviewers opinion is that its unclear why the MCMC scheme proposed is a natural or superior choice to existing approaches to training EBMs in the literature. Training EBMs have seen a resurgence of late and there have been multiple approaches that provide significant computational benefit [1] [2] [3] are few recent examples.