Venue: ML_Reproducibility_Challenge 2021 Fall

Reviews:

final_score: rating: 7: Good paper, accept
review_content: The authors have done a remarkable job of reproduction. The work incorporates major to-dos of RC.
review_title: Good work - in sync with the goal of RC

final_score: rating: 6: Marginally above acceptance threshold
review_content: This paper seems to follow the guidelines for reproducibility, notation, code.

The authors introduce a simple output layer (called "GEN") that can be used in graph neural networks to to obtain a means of predicting how a series of graphs will evolve over time.  The domain is graph editing.

The authors indicate GENs can be used to do several things such as a solution to the GED problem and the authors show GENs in experimental environments which are encouraging for predicting node insertions and edge operations.  
The manuscript reveals that with a graph matching a pair of graphs, there is an algorithmically determinable near-optimal graph edit sequence for generating training data. Finally, the paper demonstrates the model's capabilities on a set of synthetic benchmarks.

The manuscript is well written and this paper is likely of interest to the ICLR forum broadly speaking.  This would be suitable for poster presentation.   
review_title: Graph editing tool

Meta-review: Following the recommendation of reviewers and meta-reviewer, the paper is accepted for ML Reproducibility Challenge 2021, and will be published in the upcoming special edition of ReScience Journal.
Decision: Accept