Encoder architecture is a 2-layer GCN. This has limited expressive power.
Computation complexity and scalability are not analyzed theoretically, and the benchmarks used range from tiny (cora, citeseer) to medium graphs (ogb-arxiv).
Missing graph contrastive baselines in experiments and review (see detailed feedback below).
t-tests are not the best for evaluating statistical significance in this setting (see detailed feedback below).
Detailed Feedback and Questions:
This paper compares thoroughly with existing methods for handling noisy data but not nearly enough with contrastive graph learning baselines. There are several other baselines aside from GraphCL [1] that are not cited in the review and are not compared with the proposed approach in the experiments, See [2-4] ([1] is included in the paper but not its subsequent works).
Well known GCL methods include perturbations in the edge set GRACE (Zhu et al., 2020), BGRL (Thakoor et al., 2021), GBT (Bielak et al., 2021)), centrality (GCA (Zhu et al., 2021)), diffusion matrix (MVGRL (Hassani & Khasahmadi, 2020)) and the original graph (GMI (Peng et al., 2020)) and DGI (Velickovic et al., 2019)). The proposed method is compared to MVGRL (Hassani & Khasahmadi, 2020) but it is not clear why it is not compared to the others. If there is a good reason why a direct comparison does not apply that can be described in the review section or a direct experimental comparison is produced, I am willing to raise my score.
2, While there is performance improvement it in the experimental results it is often quite small. The t-tests are helpful but I think using Wilcoxon signed test would be better given small sample size. t-tests require assumptions of normality that are not explicitly justified in the paper. Do the t-tests that are close to 0.05 remain under the threshold when switching to Wilcoxon test?
Are the loss terms in eq. 6 added without weights? I would assume in general they are in different scales so weighting them should improve performance.
This work tackles feature and label noise. What about noise in the graph topology?
I understand that given extensive hyperparameter search (Table 4) gamma_0 and xi_0 can be selected. However, it would be interesting to see how selection of these values affects performance stability. How sensitive is the algorithm to xi selection in equation 2 and gamma_k selection in equation 5?
Typos: p. 1 handle an2 almost infinite” -> an p. 2 Existing Methods Handing Noisy Data" -> handLing p. 3 the attributes of each node is in” -> are p. 3 following two tasks.” -> replace .” by :”
References: [1] You, Yuning, et al. “Graph contrastive learning with augmentations.” Advances in Neural Inf. Proces. Syst. 33 (2020): 5812-5823. [2] You, Yuning, et al. “Graph contrastive learning automated.” Int. Conf. Machine Learning, 2021. [3] Qiu, Jiezhong, et al. “Gcc: Graph contrastive coding for graph neural network pre-training.” Proc. ACM SIGKDD Int. Conf. Knowledge Discovery & Data Mining. 2020.
3 following two tasks.” -> replace .” by :” References: [1] You, Yuning, et al. “Graph contrastive learning with augmentations.” Advances in Neural Inf. Proces. Syst.