Kati8/ICLR_2023_2025 huggingface dataset:
authors: Aoran Wang
authors: Jun Pang
reviews: 
Comments: The paper introduces an innovative approach to structural inference (SIDEC), employing dynamics encoding and partial correlation coefficients. The authors employ variational inference to encode node dynamics within latent variables and reorganize these variables temporally to reconstruct directed graph structures. The resulting method stands out for its scalability, accuracy, and versatility, outperforming existing approaches as demonstrated through experimental results. The paper additionally includes a comprehensive implementation appendix and provides a link to the anonymous GitHub repository hosting the SIDEC codebase, ensuring reproducibility.
Strengths: 1. The paper presents a novel approach to structural inference, combining variational dynamics encoding with partial correlation coefficients, demonstrating superior performance compared to existing methods, as validated by experimental results. The use of a variational model to encode node dynamics addresses scalability challenges from previous methods, and this application of Variational Autoencoders (VAE) in structural inference is a notable contribution.

2. The method exhibits impressive scalability, accommodating trajectories with both one-dimensional and multi-dimensional features, making it applicable to a wide range of scenarios.

3. The paper is exceptionally well-organized and articulated, employing clear and concise language throughout.

4. The authors provide a detailed explanation of their proposed method, including mathematical formulations and implementation details, enhancing its accessibility and reproducibility.

5. The evaluation is meticulously crafted and comprehensive, successfully establishing the superior performance of SIDEC in comparison to existing methods. The additional experimental results presented in the appendix further bolster the credibility and persuasiveness of SIDEC's superiority.

6. The limitations of the proposed method are conscientiously addressed in the appendix.
Weaknesses: While the paper is commendable overall, one potential weakness lies in the absence of an evaluation on real-world data. The authors acknowledge this in the "Limitations" section, citing the challenges of collecting such data. It would be beneficial to include plans for addressing this in future work.
Recommendation: 10: strong accept, should be highlighted at the conference
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 4 excellent
Clarity: 4 excellent
reviews: 
Comments: This paper proposes a new model for structure inference in dynamical systems, called SIDEC. 
It combines two methods, the Variational Dynamics Encoder (VDE) and the Partial Correlation (PCOR), to infer the directed graph structure of a dynamical system from its trajectory data. The method captures the minimum information of node feature dynamics and can effectively reduce feature dimensions. It is applicable to time-series data with multi-dimensional and one-dimensional features. It evaluates SIDEC on 20 datasets and compares it with other benchmark methods. The results show that SIDEC outperforms other methods in terms of average AUROC metric and it is robust to Gaussian noise.
Strengths: The paper introduces a novel model, SIDEC, that combines two methods, VDE and PCOR, to infer the directed graph structure of a dynamical system from its trajectory data. 
The paper provides a comprehensive introduction, background, and motivation for the problem, which make it easier to follow. 
It seems that SIDEC can capture the minimal information of node feature dynamics and effectively reduce feature dimensions, leverage temporal information to infer directed edges using conditional correlations, and perform robustly against Gaussian noise .
It evaluates SIDEC on 20 datasets and compares it with other benchmark methods and the results show that SIDEC outperforms other methods in terms of average AUROC metric.
Weaknesses: 1.	This paper mainly designs and analyzes the SIDEC framework based on information bottleneck theory and partial correlation coefficient, but it does not give clear mathematical proofs and theoretical guarantees to explain how they influence each other.
2.	Experiment Datasets are all artificially generated and may not fully reflect the complexity and diversity of the real world. Therefore, the experimental results of this article may have certain biases and limitations, and further testing and evaluation on more real data sets are required.
3.	This method combines VDB and PCOR, but the paper does not give the impact on training time after the combination.
Recommendation: 6: marginally above the acceptance threshold
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 2 fair
Clarity: 2 fair
reviews: 
Comments: This paper proposes a new method for structure inference from networked dynamical systems. At a high level, the authors' methodology is as follows. First , they use a variant of VAE's -- called a Variational Dynamics Encoder (VDE) -- to find a low-dimensional embedding of the time series of each node in the network. The network structure between nodes can then be determined by computing the partial correlation scores between the nodes' latent representations. The authors then test their methods on several benchmark datasets. Their methods perform favorably against prior approaches.
Strengths: The paper is very well written, and the approach is clearly laid out. The goal of the paper -- that of learning the network that facilitates dynamic processes -- is of wide applicability and significant interest. A major strength of this work is that the proposed framework performs better than several other prior approaches for this task.
Weaknesses: - There are some missing details about key elements of the procedure. How are the dynamics modeled, and how does the adjacency matrix A influence the dynamics? How is A estimated from the embedded representations? It's hinted that this is done by the partial correlations formula, but it doesn't seem to be explicitly stated.
- It's not intuitive to me why the node-level dynamics can be predicted from a single node's trajectory alone, without needing to know the dynamics of other nodes (which certainly influence the trajectories of the node in question). In other words, won't the accuracy of trajectory predictions be strongly affected if the VDE is applied separately to each node trajectory, rather than jointly? 
- The authors state that scalability is a major contribution of the method. But in the Experiments, the largest network has only 250 nodes, which makes me question whether there is sufficient evidence for scalability. 
- Various missing details in the Experiments section -- see the "Questions" section below.
Recommendation: 6: marginally above the acceptance threshold
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 2 fair
Clarity: 4 excellent