Venue: ICLR 2022 Conference

Reviews:

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The paper introduces the concept of node-specific information (NSI) to model that nodes in a graph may have private information that other nodes cannot have access to. The paper uses Neural Relation Inference (NRI), a framework published in 2018 based on variational inference, to uncover the hidden relations of nodes in the graph. For instance, in a driving scenario, different cars can be nodes in a graph with their publicly visible trajectory and their private information about the intention (e.g. desired destination), which is not shared with other nodes. The encoder and decoder in NRI are modified such that NSI stays private and is not shared with other nodes. The paper considers problems that require uncovering the interactions of entities in a multi-agent dynamical system. The evaluation is based on the accuracy of future trajectory prediction. The paper demonstrates the effectiveness of the idea on three different datasets, one action-conditional dataset and two goal-conditional datasets.
main_review: Strengths:
- The concept of node-specific information (NSI) seems to be novel and is interesting.
- The paper is well-written. All components are explained in detail.
- The paper demonstrate the effectiveness of the idea. The proposed approach performs better than reference systems that use the private information only at the decoder. Hence, I agree with the conclusion that the approach helps to form better interaction graphs.
- The distinction to prior works, especially NRI, is clear. It is easy for the reader to understand what the contributions of the paper are.

Weaknesses:
- The paper argues that in many real-world examples, "there is a set of hidden features that affect the way entities interact with each other." However, I am not sure how reasonable this is. For instance, in autonomous driving, it seems reasonable that considering the intention of individual entities can help to generate an interaction graph. However, it is not the hidden features that affect the interaction of the entities, but only the public nodes, which are affected by the hidden features.
- The paper uses autonomous driving as an example. However, in autonomous driving, the intention of a car can be shared with other cars via car2car communication. However, sharing this information is limited to system that are capable of this form of communication. Pedestrians, for instance, also have private intentions that cannot be shared easily.
- I am missing an analysis of the learned graphs, since the paper states that their method is better able to learn interaction graphs. Now I am wondering if this difference can be visualized in an example from one of datasets, or even better analyzed in a systematic way.


Questions:
- Question 1: One perhaps crucial part is not yet entirely clear to me: Since the public nodes are connected, and each public node is connected to its private node, isn't it possible that the private information flows to the other nodes in only 2 hops, especially in the decoder? Then, the private information can be shared with other public nodes. The paper states that "... however its [the private node's] effect might be observed in the future times steps ...". But as far as I understand, it is not only the effect of the private information but the private information itself that can be shared. It would be great if the authors or other reviewers could clarify this point.
- Question 2: Furthermore, I am wondering if the approach can actually be used in real-world situations, for instance for autonomous driving. In a real-world setting, each car, and therefore each model, has its own set of hidden information that is not shared with other cars, which means that each model only has access to the private information of a single car. However, the model assumes to have  access to all the private nodes (i.e. it has a global view of the problem, not only a local view), which seems to not fit to the envisioned application setup.
summary_of_the_review: To summarize, the idea of introducing node-specific, private information into a graph of multiple interactive systems seems to be novel and interesting and improves the prediction performance. However, it is not entirely clear to me if the private information can be propagated to other nodes (question 1) and if the model can even be applied in the envisioned scenario (question 2). I think the answers to both questions are important to make a reasonable assessment of the paper. Hence, my recommendation is rather tentative and I will update it as soon as the application setup becomes more clear to me.

After rebuttal:
Thanks a lot for the author's replies. I still think the applicability of the idea may be problematic, but I think the idea of NSI is sufficently interesting and novel to be accepted to this conference. Hence, I raise my score to accept.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: **Summary**: This paper introduces a neural relational inference model that makes use of the hidden features of each node in a variational inference framework. Specifically, the hidden/individual information is modeled as private node in the graph. Importantly, the task assumption made by the authors is that these individualized features cannot be observed by other entities. 

**Contributions**: 
1. The authors claim be to the first to study the use of individualized information for each entity in this direction.
2. The proposed approach achieve state-of-the-art results while only introducing minimum additional computational complexity. 
main_review: ## Weaknesses
* The task assumption of the paper does not look straightforward to me. I am doubtful about the practical value of the task setting: "the individualized information cannot be accessed by other entities but its effect will be observed in the future". Is this a well-recognized setting? Are there practical scenarios of this assumption?
* Given the existing work of NRI and the variational inference framework, the paper is not clear about the contributions. For instance, what are the new challenges that need to be solved after introducing the private entities? Besides the model architectures, what are the key innovations? 

## Strengths
* The experimental results look strong and thorough. Different datasets are considered and the improvements are significant. 
summary_of_the_review: Overall, this paper has demonstrated strong results on neural relational inference tasks. The concerns I have are about the assumption of the approach and unclear novelty points.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: This paper presents a neural relational inference model with node-specific information. Experiments on real-world datasets validate the merit of the proposed method.
main_review: This paper presents a neural relational inference model with node-specific information. Experiments on real-world datasets validate the merit of the proposed method. 

Stengths:
1. This paper is well written and the idea is clear. 
2. The results are promising compared to various baslines. 

Weaknesses:
1. The codes are not availabel, it is difficut for others to re-produce the results. 
summary_of_the_review: This paper presents a neural relational inference model with node-specific information. Experiments on real-world datasets validate the merit of the proposed method. 

Stengths:
1. This paper is well written and the idea is clear. 
2. The results are promising compared to various baslines. 

Weaknesses:
1. The codes are not availabel, it is difficut for others to re-produce the results.
review_title: null

Meta-review: This paper extends the Neural Relational Inference framework for probabilistic inference of interaction relations between entities, to a scenario where entities may have private features, which requires modifications of the standard graph encoders and decoders in NRI.

Reviewers appreciated both the model and the overall execution of the paper: the building blocks are clear, the evaluation does its job well. The main doubts are about the applicability of the setting, for which the authors don't provide too many examples. However, the construction is somewhat intuitive, and even in cases where private attributes aren't explicit, it may be valuable to disentangle the shareable attributes this way. We encourage the reviewers to discuss the applicability a bit further.

Typos: (not exhaustive, please doublecheck with a spell checker)
 - multiple occurrences of Gumble instead of Gumbel
 - bottom of pg 4, factorzied -> factorized
Decision: Accept (Poster)