Venue: ICLR 2022 Conference

Reviews:

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: This paper proposes a new deep learning architecture called ST-GNN that learns representations on graphs that evolve over time. Their work focuses on developing an interesting time-varying convolutional architecture, which exploits the graph-time underlying structure of the signals, processing across both the graph and time domains. The authors introduce new graph shift operators (GSO), time shift operators (TSO), followed by a linear combination of those which results in a space-time shift operator termed STSO. The advantage of this paper is that it can handle continuous-time graph signals and the stability of this proposed architecture is studied theoretically (Section 4) with a significant result outlined in Proposition 1 of the paper where the difference between the space-time graph filters of the original graph and the perturbed graph are upper bounded by the order of error introduced.   

main_review: -- Strengths:

1. The paper is very well-written and easy to follow. It was a pleasure to read the novel theoretical material presented in this paper.  

2. The paper proposes a new architecture based on their new operators and implementing the finite impulse response (FIR) filter recursively. 

3. The theoretical material in the paper is quite thorough and very detailed. In the appendix F, the authors also extend their stability analysis in the main paper to not only consider single-layer features but also extend their analysis to multiple features, which makes the theoretical contributions even stronger.

-- Weaknesses:

1. The experiments in this paper are done on synthetically generated datasets for two applications of flocking and unlabelled motion planning. I would have liked to see experiments done on more real-world mobility datasets to better understand the applicability of the proposed method. It is evident from the in-depth theoretical analysis that the author’s primary focus is to propose a novel architecture that comes with robustness guarantees. But, I feel that some experiments on real-world datasets would further strengthen this paper’s contributions.

2. Can this method also be applied to “standard prediction tasks” on time-varying graphs? If so, could you please elaborate on this and how it would compare to other existing networks for time-varying graphs, e.g. graphon neural networks?

3. If ST-GNN can also be used on standard prediction tasks and do a better job than other GNNs, I was wondering how it would handle the cases where we have heterophilous vs. homophilous time-varying networks? This would also be interesting to investigate. 

summary_of_the_review: This paper proposes a novel architecture based on graph-shift, time-shift and space-time shift operators (also proposed by the authors in the paper) to handle time-varying graphs. The theoretical analysis of this paper is very thorough and clearly elucidates the stability of their NN architecture.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper introduces a new spatio-temporal Graph Neural Network, ST-GNN, for making predictions on temporal network. Its proposed space-time convolution operator is a composition of temporal convolution and graph diffusion. The paper further proves that under practical conditions their ST-GNN with Integral Lipschitz filters is stale to small perturbation in both time and graph domain. 
main_review: Strengths:
1.	The paper is well-written and, despite heavily populated with math formulas and notation, I find it enjoyable and relatively easy to follow it through.
2.	The proposed ST-GNN is principled, natural and neat.
3.	I like the analysis of ST-GNN’s stability to perturbations. I find it new and insightful, and especially interesting to read, though I am not an expert in this so I may be biased as well.

Weaknesses:
1.	I am not sure if the experiments would be very appealing to the deep learning community because they do not compare with the many latest temporal network SOTAs, and the two tasks seem to mostly target audience from robotics and control. Technically speaking, it mainly uses temporal network dataset whose features have strong continuous-time dynamics, and only for node-level prediction task). To appeal to more audience from the ICLR community, I would suggest that the experiments be significantly extended by referencing and comparing with the following papers in recent years: [1, 2, 3, 4, 5]. Alternatively, the authors could be more explicit in restricting ST-GNN's application domain.
2.	Despite being well-principled and neat, the proposed space-time convolution (Equation 7) does not seem quite new to me. It seems essentially just a temporal convolution followed by graph diffusion which is not totally new ([6] for example adopted a very similar idea).

[1] Inductive Representation Learning on Temporal Graphs
[2] Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks
[3] EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
[4] Variational Graph Recurrent Neural Networks
[5] JODIE: Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
[6] TEDIC: Neural Modeling of Behavioral Patterns in Dynamic Social Interaction Networks

summary_of_the_review: This is a well-written paper with a neat space-time convolution model and important theoretical contributions in analyzing its stability. However, as significant as its strengths are, its weaknesses are also concerning, which mainly involves two aspects: 1. the architecture of ST-GNN does not seem novel and may suffer from varying graph size; 2. it certain could have empirical studies done on more datasets and have more baselines compared.  Overall, I would be very happy to improve the score if we could see more extensive experiment with more baselines and positive results.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper introduces a space-time convolution operator that is robust to graph perturbations and small variations of sampling rates.
main_review: Strengths:
1.  Solid theoretical support.
2.  Jointly process graph and time domains in a single convolution operation.
3.  Stable to small variations in graphs and sampling rates.

Weakness
1. Lack of comparison with other methods. 
2. Lack of time complexity analysis.
3. The setting of hyper-parameters is not specified. 

summary_of_the_review: Although this paper theoretically proves the stability of the proposed convolution operator, it does not provide experimental evidence about the superiority of the proposed method against other baseline methods. Given this paper is not the first to study time-varying graph signals, I think baseline comparison is necessary. Based on the above consideration, I think this paper is not ready for acceptance.
review_title: null

Meta-review: This paper proposes a new  time-varying convolutional architecture (ST-GNN) for dynamic graphs. The reviewers were positive about the presentation and detailed theory, especially on the stability analysis. The shared criticism was on experimental validation synthetic datasets that the reviewers did not find appealing. The AC believes that while the lacking validation concerns are legit, there is a lack of sophisticated dynamic graph benchmarks in the community yet, so the authors did their best effort to test their method. We thus recommend to accept the paper.
Decision: Accept (Poster)