Venue: ICLR 2022 Track Blog

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: **Summary of Contributions**:

The main goal of this blog post is to describe how graphs with geometric feature vectors can be seen as geometric objects in the context of geometric deep learning. In this framework, deep learning on geometric graphs translates to building E(3) and permutation invariant/equivariant layers, which is the basis of the GVP-GNN paper. By interpreting these graphs as geometric objects, the blogpost also proposes other applications where GVP-GNN can be applied, such as on geodesic data.  


**Strengths and Weaknesses**:

Strengths:
- The interpretation of graphs with geometric vectors as geometric objects fits nicely into the context of geometric deep learning (Bronstein et al.) and provides an interpretation that unifies the study of these objects.
- The blog post also provides a comparison with other methods on geometric graphs (SchNet, TFN and SE(3)-Transformer) and highlights how GVP-GNN is a more versatile method in comparison with these other algorithms.

Weaknesses:
- The blog post does not do a good job of providing a summary of the paper and introducing the background knowledge. 
- Additionally, the blog post fails to introduce the notation it uses in the equations and assumes familiarity of the reader with the notation used in the paper.


**Requested Changes**:

I recommend covering the notation and background in more detail so that going through the notation and details of the paper would not be necessary. For a reader that is not familiar with the geometric deep learning perspective, a clearer introduction to this concept and the importance of studying these graphs as geometric objects would be beneficial. 

The section which compares GVP-GNN with other methods on geometric graphs is not very clear. The authors mention how GVP-GNN is different from those methods but doesn’t make it concrete enough how this difference leads to more flexibility. It also doesn’t mention the fact that in comparison with those methods GVP-GNN uses absolute positions and not relative ones; an important detail that was also highlighted by the authors in the paper.

Overall, I believe that although the concept introduced is interesting, the blog post lacks clarity and doesn’t provide enough background for some of the topics it discusses. 

review_title: Interesting blog post analyzing GVP-GNN in the context of Geometric Deep Learning but lacks clarity 

final_score: rating: 6: Marginally above acceptance threshold
review_content: **Summary of contributions**

This blog post reassesses the ICLR 2021 paper "Learning from Protein Structure with Geometric Vector Perceptrons" from a geometric deep learning based perspective. 
While the cited paper presents itself as combining "geometric" (e.g. 3d convolutions) and "relational" (e.g. graph network) methods together, this blog post presents this method as one acting on a combined E(3) / permutation symmetric geometry ("geometric graphs"), and shows how the authors of the cited paper developed their method in line with the Geometric Deep Learning framework proposed by Bronstein et al. 

By stepping back and viewing this method as applying to the more general "geometric graphs" object rather than just proteins, the blog post further proposes other potential applications of this method, something the cited paper does not do, and highlights the utility of this method.

**Strengths and weaknesses**

Strengths:
- Tying in a new work into the geometric deep learning framework is useful to researchers, providing a concrete example of the geometric deep learning blueprint in action
- By generalizing the approach to general geometric graphs instead of focusing on molecules, this blog post is able to identify other problem domains relating to geometric graphs and thoughtfully consider the implications of applying the cited paper's methods to these tasks
- Comparisons with other neural network architectures applied to geometric graphs provide helpful context for the original paper

Weaknesses:
- Terminology and notation is used without introduction. For example, when describing amino acid features, it is not at all clear what $C\alpha_j$  represents (until later on in the figure caption). When providing a refresher on GNNs, notation is not clarified at all.
- Does not sufficiently introduce the geometric deep learning blueprint, given that this is a main focus of the blog post.

**Requested changes**

- Further introduction of the Geometric Deep Learning blueprint for those unfamiliar with it would be very helpful for researchers
- Providing references for graph drawing would be very useful
- There are a number of grammar and spelling mistakes e.g. "Nodes and edges are usually features on them for GNNs to learn from" and "neighboring amnio acids"
review_title: Interesting broader perspective on geometric graphs, but explanations could be improved

Decision: Accept