Equivariant Entity-Relationship Networks
Due to its extensive use in databases, the relational model is ubiquitous in representing big-data. However, recent progress in deep learning with relational data has been focused on (knowledge) graphs. In this paper we propose Equivariant Entity-Relationship Networks, the class of parameter-sharing neural networks derived from the entity-relationship model. We prove that our proposed feed-forward layer is the most expressive linear layer under the given equivariance constraints, and subsumes recently introduced equivariant models for sets, exchangeable tensors, and graphs. The proposed feed-forward layer has linear complexity in the the data and can be used for both inductive and transductive reasoning about relational databases, including database embedding, and the prediction of missing records. This, provides a principled theoretical foundation for the application of deep learning to one of the most abundant forms of data.
This paper defines a parameter-tying scheme for a general feed-forward network which respects the equivalence properties of a vector encoding of relational (ie database) data. As long as this parameter-tying is observed, the resultant feed-forward network is guaranteed to respect the equivariant properties of this data, meaning entity indexes can be permuted, and the output produced by the layer will be permuted in the correct manner. This results in a maximally expressive feed forward layer for this kind of data. This is a very good paper and should be accepted. The approach is convincing, and provides a clear way forward for representing an important class of data. The paper is surprisingly well-written and comprehensible given the complexity of the math. I learned a lot from reading this paper. The experiments are somewhat rudimentary, but they are perfectly adequate for a paper whose contribution is primarily methodological and theoretical. One suggestion I have is that some of the details from the Appendix should be moved into the paper proper, most notably the details of the experiments in Appendix H.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Equivariant Entity-Relationship Networks
Due to its extensive use in databases, the relational model is ubiquitous in representing big-data. However, recent progress in deep learning with relational data has been focused on (knowledge) graphs. In this paper we propose Equivariant Entity-Relationship Networks, the class of parameter-sharing neural networks derived from the entity-relationship model. We prove that our proposed feed-forward layer is the most expressive linear layer under the given equivariance constraints, and subsumes recently introduced equivariant models for sets, exchangeable tensors, and graphs. The proposed feed-forward layer has linear complexity in the the data and can be used for both inductive and transductive reasoning about relational databases, including database embedding, and the prediction of missing records. This, provides a principled theoretical foundation for the application of deep learning to one of the most abundant forms of data.
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>



This paper defines a parameter-tying scheme for a general feed-forward network which respects the equivalence properties of a vector encoding of relational (ie database) data. As long as this parameter-tying is observed, the resultant feed-forward network is guaranteed to respect the equivariant properties of this data, meaning entity indexes can be permuted, and the output produced by the layer will be permuted in the correct manner. This results in a maximally expressive feed forward layer for this kind of data. This is a very good paper and should be accepted. The approach is convincing, and provides a clear way forward for representing an important class of data. The paper is surprisingly well-written and comprehensible given the complexity of the math. I learned a lot from reading this paper. The experiments are somewhat rudimentary, but they are perfectly adequate for a paper whose contribution is primarily methodological and theoretical. One suggestion I have is that some of the details from the Appendix should be moved into the paper proper, most notably the details of the experiments in Appendix H.
assistant
This paper defines a parameter-tying scheme for a general feed-forward network which respects the equivalence properties of a vector encoding of relational (ie database) data. As long as this parameter-tying is observed, the resultant feed-forward network is guaranteed to respect the equivariant properties of this data, meaning entity indexes can be permuted, and the output produced by the layer will be permuted in the correct manner. This results in a maximally expressive feed forward layer for this kind of data. This is a very good paper and should be accepted. The approach is convincing, and provides a clear way forward for representing an important class of data. The paper is surprisingly well-written and comprehensible given the complexity of the math. I learned a lot from reading this paper. The experiments are somewhat rudimentary, but they are perfectly adequate for a paper whose contribution is primarily methodological and theoretical. One suggestion I have is that some of the details from the Appendix should be moved into the paper proper, most notably the details of the experiments in Appendix H.