Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This work studies the patio-temporal distribution shift issue of dynamic graph neural networks. To pursue the robustness of DyGNNs, the authors proposed a specific invariant learning method and conducted experiments on both real-world and synthetic datasets.
strengths_and_weaknesses: Strong points:
1. This paper revels the impact of distribution drift in DyGNNs, which forms a new research problem.
2. This paper presents a new method for training distributionally robust DyGNNs.
3. Extensive experiments validate the effectiveness of the proposed method.

Weak points:
1. The experiment results need more explanations. For instance, why IRM and GroupDRO achieve inferior performance under the "w/ DS" setting. Why the compared methods show different trends on the real-world and synthetic datasets, e.g., GCRN performs quite well on the synthetic datasets.
2. As compared to [18], what is the advantage of the proposed method.

questions: 1. The experiment results need more explanations. For instance, why IRM and GroupDRO achieve inferior performance under the "w/ DS" setting. Why the compared methods show different trends on the real-world and synthetic datasets, e.g., GCRN performs quite well on the synthetic datasets.
2. As compared to [18], what is the advantage of the proposed method.

limitations: No.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper investigates graph neural networks on dynamic graphs, especially under spatio-temporal distribution shifts. The authors recognize that distribution shift is an important factor for dynamic graph embedding, which is not well-handled by the existing approaches. To address this, the authors propose a novel model named DIDA, to handle spatio-temporal distribution shifts in dynamic graphs by discovering and fully utilizing invariant spatio-temporal patterns. Experiments on four datasets demonstrate the effectiveness of the proposed model.
strengths_and_weaknesses: Strengths:

1. The paper is well-written and easy to follow.
2. Dealing with the spatio-temporal information on dynamic graphs from the perspective of discovering and utilizing invariant patterns, I feel might be an effective direction.
3. The experiments are sufficient to demonstrate the performance of the proposed model.


Weaknesses:

1. The related studies discussed in Related Work are not quite sufficient. I suggest the authors cite and discuss more.
2. It is better to give the details for the baselines (e.g., the differences with the proposed model), as well as more details for the datasets (e.g., give a statistic table) in the main paper. 
questions: Please see the weaknesses.
limitations: None
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This paper introduces a method of dynamic graph neural networks with spatio and temporal intervention mechanism.
strengths_and_weaknesses: Strength:
(1) the empirical study shows considerable improvement on existing method.
(2) Innovative using attention layers to capture spatio-temporal information.

Weakness:
(1) no strict proof or detailed illustration to show why spatio-temporal intervention works.
(2) computational complexity is not discussed in the main contents.
questions: in equation (6), why are the expressions for m_i and m_v identital? 

limitations: I failed to find the accurate definition of 'ego-graph', 'distribution shifts', 'invariant and variance structural patterns' , etc. As a result, it is not easy to understand this paper correctly without reading several previous papers.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper studies the problem of spatio-temporal distribution shift in dynamic graphs.
By disentangling the patterns in dynamic graphs into invariant and variant ones, the invariant patterns are utilized for stable prediction and the impact of distribution shift can be reduced.
Although the distribution shift has been widely studied in the literature on computer vision and natural language processing, the authors made early attempts in the dynamic graph.
I am overall positive about this work.
strengths_and_weaknesses: Strengths

[+] The distribution shift in the dynamic graph is critical and the authors made the early attempts on this topic, which should be encouraged in the community.

[+] The proposed solution is technical sound, the disentanglement as well as the causal spatio-temporal intervention mechanism can satisfy the requirements.

[+] The experiments are extensive and the results are encouraging.

Weaknesses

[-] The invariant pattern is assumed to be dependent on the time. From my opinion of view, the invariant pattern can be futher divided into the time dependent and time independent ones.

[-] The distribution shift in the experimental datasets are manually conducted，it would be better to have some automatically designed mechanism. 
questions: 1. Can the proposed model generalized to the continuous dynamic graph?
2. What is the variant and invariant pattern in dynamic graph? Is there any common understanding rather than the specific graph type?
limitations: The core definition of variant and invariant are not well explained, which limits the generalization and scalability of the proposed method.
review_title: null

Meta-review: metareview: The paper addresses spatio-temporal distribution shifts in dynamic graphs by discovering and utilizing invariant patterns, i.e., structures and features whose predictive abilities are stable across distribution shifts. The paper is an early try to address distribution shifts in dynamic graphs, which is an interesting and important problem. The experiments show the effectiveness of the proposed methods on synthetic and real-world graphs. The authors are strongly encouraged to add more discussion on the experiments and baselines, related work, definitions of 'ego-graph', 'distribution shifts', 'invariant and variance structural patterns', computational complexity, and the other clarifications requested by the reviewers in the final version.
confidence: Certain
Decision: Accept