Kati8/ICLR_2023_2025 huggingface dataset:
authors: Richard Bergna
authors: Sergio Calvo Ordoñez
authors: Felix Opolka
authors: Pietro Lio
authors: José Miguel Hernández-Lobato
reviews: 
Comments: The paper proposes a new GNN architecture version based on stochastic differential equations to improve uncertainty estimation.
Strengths: 1. The paper demonstrates good empirical performance.
2. The authors evaluate uncertainty estimation through OOD detection, noise perturbation, and active learning.
Weaknesses: 1. The paper lacks comparisons with SOTA works. Some strong methods, such as GPN (Stadler et al., 2021) and GNSD (Lin et al., 2024), are mentioned but not evaluated, despite their better empirical performance compared to the baselines used. Additionally, other high-performing methods that utilize energy variants are not tested.
2. The method shows significant similarities to Lin et al. (2024), who also propose an SDE-based GNN. While the authors acknowledge the difference with one sentence in the related work section, I believe the similarities and differences should be further discussed in detail, such as by comparing the frameworks mathematically and conducting a specific study (even on a synthetic dataset) to highlight the unique merits of their approach. But the method is not compared against at all.
Recommendation: 6
Reviewer confidence: 3
Soundness/correctness: 2
Clarity: 2
reviews: 
Comments: This paper generalizes GNODE to its stochastic counterpart, using SDEs to derive uncertainty-aware representations for graph-structured data. Theoretical and experimental characterization of the framework, named LGNSDE, showed its robustness and uncertainty quantification capability.
Strengths: - The idea is sound and innovative.
- The mathematical framework is simply elegant.
- The robustness of this model is clearly demonstrated by theoretical and experimental results.
- This framework can be of high utility to the community.
Weaknesses: - I would imagine the result is heavily dependent upon the integration methods. See the extensive study conducted in GRAND: Graph Neural Diffusion, Chamberlain et al. 2021. 
- The accuracy of this model is not as performant as many cheaper variants.
- I would love to see its speed and memory benchmark, as I imagine it to be quite expensive.
Recommendation: 10
Reviewer confidence: 3
Soundness/correctness: 4
Clarity: 4