Relational Graph Attention Networks
We investigate Relational Graph Attention Networks, a class of models that extends non-relational graph attention mechanisms to incorporate relational information, opening up these methods to a wider variety of problems. A thorough evaluation of these models is performed, and comparisons are made against established benchmarks. To provide a meaningful comparison, we retrain Relational Graph Convolutional Networks, the spectral counterpart of Relational Graph Attention Networks, and evaluate them under the same conditions. We find that Relational Graph Attention Networks perform worse than anticipated, although some configurations are marginally beneficial for modelling molecular properties. We provide insights as to why this may be, and suggest both modifications to evaluation strategies, as well as directions to investigate for future work.
 The paper proposes a few variations on the RGCN model with adding attention to either within relations or across relations. Unfortunately the paper falls short in two main areas: - novelty: the additions proposed are small modifications to existing algorithms and there are other methods of attention on graphs which have been discussed in the paper but not directly compared to (e.g. this method builds heavily on Velickovic et al 2017) - impact: the results achieved in the experiments are very small improvements compared to the baseline of RGCN (~ +0.01 in two experiments and ~ -0.04 in another) and often these small variations in results can be compensated with better baselines training (e.g. better hyper-params, ...) However, on a positive note, the paper has been written very well and I really liked the frank discussion on page 8 about results on MUTAG dataset.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Relational Graph Attention Networks
We investigate Relational Graph Attention Networks, a class of models that extends non-relational graph attention mechanisms to incorporate relational information, opening up these methods to a wider variety of problems. A thorough evaluation of these models is performed, and comparisons are made against established benchmarks. To provide a meaningful comparison, we retrain Relational Graph Convolutional Networks, the spectral counterpart of Relational Graph Attention Networks, and evaluate them under the same conditions. We find that Relational Graph Attention Networks perform worse than anticipated, although some configurations are marginally beneficial for modelling molecular properties. We provide insights as to why this may be, and suggest both modifications to evaluation strategies, as well as directions to investigate for future work.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 The paper proposes a few variations on the RGCN model with adding attention to either within relations or across relations. Unfortunately the paper falls short in two main areas: - novelty: the additions proposed are small modifications to existing algorithms and there are other methods of attention on graphs which have been discussed in the paper but not directly compared to (e.g. this method builds heavily on Velickovic et al 2017) - impact: the results achieved in the experiments are very small improvements compared to the baseline of RGCN (~ +0.01 in two experiments and ~ -0.04 in another) and often these small variations in results can be compensated with better baselines training (e.g. better hyper-params, ...) However, on a positive note, the paper has been written very well and I really liked the frank discussion on page 8 about results on MUTAG dataset.
assistant
 The paper proposes a few variations on the RGCN model with adding attention to either within relations or across relations. Unfortunately the paper falls short in two main areas: - novelty: the additions proposed are small modifications to existing algorithms and there are other methods of attention on graphs which have been discussed in the paper but not directly compared to (e.g. this method builds heavily on Velickovic et al 2017) - impact: the results achieved in the experiments are very small improvements compared to the baseline of RGCN (~ +0.01 in two experiments and ~ -0.04 in another) and often these small variations in results can be compensated with better baselines training (e.g. better hyper-params, ...) However, on a positive note, the paper has been written very well and I really liked the frank discussion on page 8 about results on MUTAG dataset.